test

Thin-Plate-Spline-Motion-Model/LICENSE

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+MIT License
+
+Copyright (c) 2021 yoyo-nb
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

Thin-Plate-Spline-Motion-Model/README.md

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+# [CVPR2022] Thin-Plate Spline Motion Model for Image Animation
+
+[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](LICENSE)
+![stars](https://img.shields.io/github/stars/yoyo-nb/Thin-Plate-Spline-Motion-Model.svg?style=flat)
+![GitHub repo size](https://img.shields.io/github/repo-size/yoyo-nb/Thin-Plate-Spline-Motion-Model.svg)
+
+Source code of the CVPR'2022 paper "Thin-Plate Spline Motion Model for Image Animation"
+
+[**Paper**](https://arxiv.org/abs/2203.14367) **|** [**Supp**](https://cloud.tsinghua.edu.cn/f/f7b8573bb5b04583949f/?dl=1)
+
+### Example animation
+
+![vox](assets/vox.gif)
+![ted](assets/ted.gif)
+
+**PS**: The paper trains the model for 100 epochs for a fair comparison. You can use more data and train for more epochs to get better performance.
+
+
+### Web demo for animation
+- Integrated into [Huggingface Spaces 🤗](https://huggingface.co/spaces) using [Gradio](https://github.com/gradio-app/gradio). Try out the Web Demo: [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model)
+- Try the web demo for animation here: [![Replicate](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model/badge)](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model)
+- Google Colab: [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1DREfdpnaBhqISg0fuQlAAIwyGVn1loH_?usp=sharing)
+
+### Pre-trained models
+- ~~[Tsinghua Cloud](https://cloud.tsinghua.edu.cn/d/30ab8765da364fefa101/)~~
+- [Yandex](https://disk.yandex.com/d/bWopgbGj1ZUV1w)
+- [Google Drive](https://drive.google.com/drive/folders/1pNDo1ODQIb5HVObRtCmubqJikmR7VVLT?usp=sharing)
+- [Baidu Yun](https://pan.baidu.com/s/1hnXmDpIbRC6WqE3tF9c5QA?pwd=1234)
+
+### Installation
+
+We support ```python3```.(Recommended version is Python 3.9).
+To install the dependencies run:
+```bash
+pip install -r requirements.txt
+```
+
+
+### YAML configs
+ 
+There are several configuration files one for each `dataset` in the `config` folder named as ```config/dataset_name.yaml```. 
+
+See description of the parameters in the ```config/taichi-256.yaml```.
+
+### Datasets
+
+1) **MGif**. Follow [Monkey-Net](https://github.com/AliaksandrSiarohin/monkey-net).
+
+2) **TaiChiHD** and **VoxCeleb**. Follow instructions from [video-preprocessing](https://github.com/AliaksandrSiarohin/video-preprocessing). 
+
+3) **TED-talks**. Follow instructions from [MRAA](https://github.com/snap-research/articulated-animation).
+
+Here are **VoxCeleb**, **TaiChiHD** and **TED-talks**  pre-processed datasets used in the paper. [Baidu Yun](https://pan.baidu.com/s/1HKJOtXBIiP_tlLiFbzn3oA?pwd=x7xv)
+Download all files under the folder, then merge the files and decompress, for example:
+```bash
+cat vox.tar.* > vox.tar
+tar xvf vox.tar
+```
+
+
+### Training
+To train a model on specific dataset run:
+```
+CUDA_VISIBLE_DEVICES=0,1 python run.py --config config/dataset_name.yaml --device_ids 0,1
+```
+A log folder named after the timestamp will be created. Checkpoints, loss values, reconstruction results will be saved to this folder.
+
+
+#### Training AVD network
+To train a model on specific dataset run:
+```
+CUDA_VISIBLE_DEVICES=0 python run.py --mode train_avd --checkpoint '{checkpoint_folder}/checkpoint.pth.tar' --config config/dataset_name.yaml
+```
+Checkpoints, loss values, reconstruction results will be saved to `{checkpoint_folder}`.
+
+
+
+### Evaluation on video reconstruction
+
+To evaluate the reconstruction performance run:
+```
+CUDA_VISIBLE_DEVICES=0 python run.py --mode reconstruction --config config/dataset_name.yaml --checkpoint '{checkpoint_folder}/checkpoint.pth.tar'
+```
+The `reconstruction` subfolder will be created in `{checkpoint_folder}`.
+The generated video will be stored to this folder, also generated videos will be stored in ```png``` subfolder in loss-less '.png' format for evaluation.
+To compute metrics, follow instructions from [pose-evaluation](https://github.com/AliaksandrSiarohin/pose-evaluation).
+
+
+### Image animation demo
+- notebook: `demo.ipynb`, edit the config cell and run for image animation.
+- python:
+```bash
+CUDA_VISIBLE_DEVICES=0 python demo.py --config config/vox-256.yaml --checkpoint checkpoints/vox.pth.tar --source_image ./source.jpg --driving_video ./driving.mp4
+```
+
+# Acknowledgments
+The main code is based upon [FOMM](https://github.com/AliaksandrSiarohin/first-order-model) and [MRAA](https://github.com/snap-research/articulated-animation)
+
+Thanks for the excellent works!
+
+And Thanks to:
+
+- [@chenxwh](https://github.com/chenxwh): Add Web Demo & Docker environment [![Replicate](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model/badge)](https://replicate.com/yoyo-nb/thin-plate-spline-motion-model) 
+
+- [@TalkUHulk](https://github.com/TalkUHulk): The C++/Python demo is provided in [Image-Animation-Turbo-Boost](https://github.com/TalkUHulk/Image-Animation-Turbo-Boost)
+
+- [@AK391](https://github.com/AK391): Add huggingface web demo [![Hugging Face Spaces](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue)](https://huggingface.co/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model)

Thin-Plate-Spline-Motion-Model/augmentation.py

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+"""
+Code from https://github.com/hassony2/torch_videovision
+"""
+
+import numbers
+
+import random
+import numpy as np
+import PIL
+
+from skimage.transform import resize, rotate
+import torchvision
+
+import warnings
+
+from skimage import img_as_ubyte, img_as_float
+
+
+def crop_clip(clip, min_h, min_w, h, w):
+    if isinstance(clip[0], np.ndarray):
+        cropped = [img[min_h:min_h + h, min_w:min_w + w, :] for img in clip]
+
+    elif isinstance(clip[0], PIL.Image.Image):
+        cropped = [
+            img.crop((min_w, min_h, min_w + w, min_h + h)) for img in clip
+            ]
+    else:
+        raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                        'but got list of {0}'.format(type(clip[0])))
+    return cropped
+
+
+def pad_clip(clip, h, w):
+    im_h, im_w = clip[0].shape[:2]
+    pad_h = (0, 0) if h < im_h else ((h - im_h) // 2, (h - im_h + 1) // 2)
+    pad_w = (0, 0) if w < im_w else ((w - im_w) // 2, (w - im_w + 1) // 2)
+
+    return np.pad(clip, ((0, 0), pad_h, pad_w, (0, 0)), mode='edge')
+
+
+def resize_clip(clip, size, interpolation='bilinear'):
+    if isinstance(clip[0], np.ndarray):
+        if isinstance(size, numbers.Number):
+            im_h, im_w, im_c = clip[0].shape
+            # Min spatial dim already matches minimal size
+            if (im_w <= im_h and im_w == size) or (im_h <= im_w
+                                                   and im_h == size):
+                return clip
+            new_h, new_w = get_resize_sizes(im_h, im_w, size)
+            size = (new_w, new_h)
+        else:
+            size = size[1], size[0]
+
+        scaled = [
+            resize(img, size, order=1 if interpolation == 'bilinear' else 0, preserve_range=True,
+                   mode='constant', anti_aliasing=True) for img in clip
+            ]
+    elif isinstance(clip[0], PIL.Image.Image):
+        if isinstance(size, numbers.Number):
+            im_w, im_h = clip[0].size
+            # Min spatial dim already matches minimal size
+            if (im_w <= im_h and im_w == size) or (im_h <= im_w
+                                                   and im_h == size):
+                return clip
+            new_h, new_w = get_resize_sizes(im_h, im_w, size)
+            size = (new_w, new_h)
+        else:
+            size = size[1], size[0]
+        if interpolation == 'bilinear':
+            pil_inter = PIL.Image.NEAREST
+        else:
+            pil_inter = PIL.Image.BILINEAR
+        scaled = [img.resize(size, pil_inter) for img in clip]
+    else:
+        raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                        'but got list of {0}'.format(type(clip[0])))
+    return scaled
+
+
+def get_resize_sizes(im_h, im_w, size):
+    if im_w < im_h:
+        ow = size
+        oh = int(size * im_h / im_w)
+    else:
+        oh = size
+        ow = int(size * im_w / im_h)
+    return oh, ow
+
+
+class RandomFlip(object):
+    def __init__(self, time_flip=False, horizontal_flip=False):
+        self.time_flip = time_flip
+        self.horizontal_flip = horizontal_flip
+
+    def __call__(self, clip):
+        if random.random() < 0.5 and self.time_flip:
+            return clip[::-1]
+        if random.random() < 0.5 and self.horizontal_flip:
+            return [np.fliplr(img) for img in clip]
+
+        return clip
+
+
+class RandomResize(object):
+    """Resizes a list of (H x W x C) numpy.ndarray to the final size
+    The larger the original image is, the more times it takes to
+    interpolate
+    Args:
+    interpolation (str): Can be one of 'nearest', 'bilinear'
+    defaults to nearest
+    size (tuple): (widht, height)
+    """
+
+    def __init__(self, ratio=(3. / 4., 4. / 3.), interpolation='nearest'):
+        self.ratio = ratio
+        self.interpolation = interpolation
+
+    def __call__(self, clip):
+        scaling_factor = random.uniform(self.ratio[0], self.ratio[1])
+
+        if isinstance(clip[0], np.ndarray):
+            im_h, im_w, im_c = clip[0].shape
+        elif isinstance(clip[0], PIL.Image.Image):
+            im_w, im_h = clip[0].size
+
+        new_w = int(im_w * scaling_factor)
+        new_h = int(im_h * scaling_factor)
+        new_size = (new_w, new_h)
+        resized = resize_clip(
+            clip, new_size, interpolation=self.interpolation)
+
+        return resized
+
+
+class RandomCrop(object):
+    """Extract random crop at the same location for a list of videos
+    Args:
+    size (sequence or int): Desired output size for the
+    crop in format (h, w)
+    """
+
+    def __init__(self, size):
+        if isinstance(size, numbers.Number):
+            size = (size, size)
+
+        self.size = size
+
+    def __call__(self, clip):
+        """
+        Args:
+        img (PIL.Image or numpy.ndarray): List of videos to be cropped
+        in format (h, w, c) in numpy.ndarray
+        Returns:
+        PIL.Image or numpy.ndarray: Cropped list of videos
+        """
+        h, w = self.size
+        if isinstance(clip[0], np.ndarray):
+            im_h, im_w, im_c = clip[0].shape
+        elif isinstance(clip[0], PIL.Image.Image):
+            im_w, im_h = clip[0].size
+        else:
+            raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                            'but got list of {0}'.format(type(clip[0])))
+
+        clip = pad_clip(clip, h, w)
+        im_h, im_w = clip.shape[1:3]
+        x1 = 0 if h == im_h else random.randint(0, im_w - w)
+        y1 = 0 if w == im_w else random.randint(0, im_h - h)
+        cropped = crop_clip(clip, y1, x1, h, w)
+
+        return cropped
+
+
+class RandomRotation(object):
+    """Rotate entire clip randomly by a random angle within
+    given bounds
+    Args:
+    degrees (sequence or int): Range of degrees to select from
+    If degrees is a number instead of sequence like (min, max),
+    the range of degrees, will be (-degrees, +degrees).
+    """
+
+    def __init__(self, degrees):
+        if isinstance(degrees, numbers.Number):
+            if degrees < 0:
+                raise ValueError('If degrees is a single number,'
+                                 'must be positive')
+            degrees = (-degrees, degrees)
+        else:
+            if len(degrees) != 2:
+                raise ValueError('If degrees is a sequence,'
+                                 'it must be of len 2.')
+
+        self.degrees = degrees
+
+    def __call__(self, clip):
+        """
+        Args:
+        img (PIL.Image or numpy.ndarray): List of videos to be cropped
+        in format (h, w, c) in numpy.ndarray
+        Returns:
+        PIL.Image or numpy.ndarray: Cropped list of videos
+        """
+        angle = random.uniform(self.degrees[0], self.degrees[1])
+        if isinstance(clip[0], np.ndarray):
+            rotated = [rotate(image=img, angle=angle, preserve_range=True) for img in clip]
+        elif isinstance(clip[0], PIL.Image.Image):
+            rotated = [img.rotate(angle) for img in clip]
+        else:
+            raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                            'but got list of {0}'.format(type(clip[0])))
+
+        return rotated
+
+
+class ColorJitter(object):
+    """Randomly change the brightness, contrast and saturation and hue of the clip
+    Args:
+    brightness (float): How much to jitter brightness. brightness_factor
+    is chosen uniformly from [max(0, 1 - brightness), 1 + brightness].
+    contrast (float): How much to jitter contrast. contrast_factor
+    is chosen uniformly from [max(0, 1 - contrast), 1 + contrast].
+    saturation (float): How much to jitter saturation. saturation_factor
+    is chosen uniformly from [max(0, 1 - saturation), 1 + saturation].
+    hue(float): How much to jitter hue. hue_factor is chosen uniformly from
+    [-hue, hue]. Should be >=0 and <= 0.5.
+    """
+
+    def __init__(self, brightness=0, contrast=0, saturation=0, hue=0):
+        self.brightness = brightness
+        self.contrast = contrast
+        self.saturation = saturation
+        self.hue = hue
+
+    def get_params(self, brightness, contrast, saturation, hue):
+        if brightness > 0:
+            brightness_factor = random.uniform(
+                max(0, 1 - brightness), 1 + brightness)
+        else:
+            brightness_factor = None
+
+        if contrast > 0:
+            contrast_factor = random.uniform(
+                max(0, 1 - contrast), 1 + contrast)
+        else:
+            contrast_factor = None
+
+        if saturation > 0:
+            saturation_factor = random.uniform(
+                max(0, 1 - saturation), 1 + saturation)
+        else:
+            saturation_factor = None
+
+        if hue > 0:
+            hue_factor = random.uniform(-hue, hue)
+        else:
+            hue_factor = None
+        return brightness_factor, contrast_factor, saturation_factor, hue_factor
+
+    def __call__(self, clip):
+        """
+        Args:
+        clip (list): list of PIL.Image
+        Returns:
+        list PIL.Image : list of transformed PIL.Image
+        """
+        if isinstance(clip[0], np.ndarray):
+            brightness, contrast, saturation, hue = self.get_params(
+                self.brightness, self.contrast, self.saturation, self.hue)
+
+            # Create img transform function sequence
+            img_transforms = []
+            if brightness is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness))
+            if saturation is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation))
+            if hue is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue))
+            if contrast is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast))
+            random.shuffle(img_transforms)
+            img_transforms = [img_as_ubyte, torchvision.transforms.ToPILImage()] + img_transforms + [np.array,
+                                                                                                     img_as_float]
+
+            with warnings.catch_warnings():
+                warnings.simplefilter("ignore")
+                jittered_clip = []
+                for img in clip:
+                    jittered_img = img
+                    for func in img_transforms:
+                        jittered_img = func(jittered_img)
+                    jittered_clip.append(jittered_img.astype('float32'))
+        elif isinstance(clip[0], PIL.Image.Image):
+            brightness, contrast, saturation, hue = self.get_params(
+                self.brightness, self.contrast, self.saturation, self.hue)
+
+            # Create img transform function sequence
+            img_transforms = []
+            if brightness is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_brightness(img, brightness))
+            if saturation is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_saturation(img, saturation))
+            if hue is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_hue(img, hue))
+            if contrast is not None:
+                img_transforms.append(lambda img: torchvision.transforms.functional.adjust_contrast(img, contrast))
+            random.shuffle(img_transforms)
+
+            # Apply to all videos
+            jittered_clip = []
+            for img in clip:
+                for func in img_transforms:
+                    jittered_img = func(img)
+                jittered_clip.append(jittered_img)
+
+        else:
+            raise TypeError('Expected numpy.ndarray or PIL.Image' +
+                            'but got list of {0}'.format(type(clip[0])))
+        return jittered_clip
+
+
+class AllAugmentationTransform:
+    def __init__(self, resize_param=None, rotation_param=None, flip_param=None, crop_param=None, jitter_param=None):
+        self.transforms = []
+
+        if flip_param is not None:
+            self.transforms.append(RandomFlip(**flip_param))
+
+        if rotation_param is not None:
+            self.transforms.append(RandomRotation(**rotation_param))
+
+        if resize_param is not None:
+            self.transforms.append(RandomResize(**resize_param))
+
+        if crop_param is not None:
+            self.transforms.append(RandomCrop(**crop_param))
+
+        if jitter_param is not None:
+            self.transforms.append(ColorJitter(**jitter_param))
+
+    def __call__(self, clip):
+        for t in self.transforms:
+            clip = t(clip)
+        return clip

Thin-Plate-Spline-Motion-Model/cog.yaml

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+build:
+  cuda: "11.0"
+  gpu: true
+  python_version: "3.8"
+  system_packages:
+    - "libgl1-mesa-glx"
+    - "libglib2.0-0"
+    - "ninja-build"
+  python_packages:
+    - "ipython==7.21.0"
+    - "torch==1.10.1"
+    - "torchvision==0.11.2"
+    - "cffi==1.14.6"
+    - "cycler==0.10.0"
+    - "decorator==5.1.0"
+    - "face-alignment==1.3.5"
+    - "imageio==2.9.0"
+    - "imageio-ffmpeg==0.4.5"
+    - "kiwisolver==1.3.2"
+    - "matplotlib==3.4.3"
+    - "networkx==2.6.3"
+    - "numpy==1.20.3"
+    - "pandas==1.3.3"
+    - "Pillow==8.3.2"
+    - "pycparser==2.20"
+    - "pyparsing==2.4.7"
+    - "python-dateutil==2.8.2"
+    - "pytz==2021.1"
+    - "PyWavelets==1.1.1"
+    - "PyYAML==5.4.1"
+    - "scikit-image==0.18.3"
+    - "scikit-learn==1.0"
+    - "scipy==1.7.1"
+    - "six==1.16.0"
+    - "tqdm==4.62.3"
+    - "cmake==3.21.3"
+  run:
+    - pip install dlib
+
+predict: "predict.py:Predictor"

Thin-Plate-Spline-Motion-Model/config/mgif-256.yaml

@@ -0,0 +1,75 @@
+dataset_params:
+  root_dir: ../moving-gif
+  frame_shape: null
+  id_sampling: False
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    crop_param:
+      size: [256, 256]
+    resize_param:
+      ratio: [0.9, 1.1]
+    jitter_param:
+      hue: 0.5
+
+model_params:
+  common_params:
+    num_tps: 10
+    num_channels: 3
+    bg: False
+    multi_mask: True
+  generator_params:
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 3
+  dense_motion_params:
+    block_expansion: 64
+    max_features: 1024
+    num_blocks: 5
+    scale_factor: 0.25
+  avd_network_params:
+    id_bottle_size: 128
+    pose_bottle_size: 128
+
+
+train_params:
+  num_epochs: 100
+  num_repeats: 50
+  epoch_milestones: [70, 90]
+  lr_generator: 2.0e-4
+  batch_size: 28
+  scales: [1, 0.5, 0.25, 0.125]
+  dataloader_workers: 12
+  checkpoint_freq: 50
+  dropout_epoch: 35
+  dropout_maxp: 0.5
+  dropout_startp: 0.2
+  dropout_inc_epoch: 10
+  bg_start: 0
+  transform_params:
+    sigma_affine: 0.05
+    sigma_tps: 0.005
+    points_tps: 5
+  loss_weights:
+    perceptual: [10, 10, 10, 10, 10]
+    equivariance_value: 10
+    warp_loss: 10
+    bg: 10
+
+train_avd_params:
+  num_epochs: 100
+  num_repeats: 50
+  batch_size: 256
+  dataloader_workers: 24
+  checkpoint_freq: 10
+  epoch_milestones: [70, 90]
+  lr: 1.0e-3
+  lambda_shift: 1
+  lambda_affine: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

Thin-Plate-Spline-Motion-Model/config/taichi-256.yaml

@@ -0,0 +1,134 @@
+# Dataset parameters
+# Each dataset should contain 2 folders train and test
+# Each video can be represented as:
+#   - an image of concatenated frames
+#   - '.mp4' or '.gif'
+#   - folder with all frames from a specific video
+# In case of Taichi. Same (youtube) video can be splitted in many parts (chunks). Each part has a following
+# format (id)#other#info.mp4. For example '12335#adsbf.mp4' has an id 12335. In case of TaiChi id stands for youtube
+# video id.
+dataset_params:
+  # Path to data, data can be stored in several formats: .mp4 or .gif videos, stacked .png images or folders with frames.
+  root_dir: ../taichi
+  # Image shape, needed for staked .png format.
+  frame_shape: null
+  # In case of TaiChi single video can be splitted in many chunks, or the maybe several videos for single person.
+  # In this case epoch can be a pass over different videos (if id_sampling=True) or over different chunks (if id_sampling=False)
+  # If the name of the video '12335#adsbf.mp4' the id is assumed to be 12335
+  id_sampling: True
+  # Augmentation parameters see augmentation.py for all posible augmentations
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    jitter_param:
+      brightness: 0.1
+      contrast: 0.1
+      saturation: 0.1
+      hue: 0.1
+
+# Defines model architecture
+model_params:
+  common_params:
+    # Number of TPS transformation
+    num_tps: 10
+    # Number of channels per image
+    num_channels: 3
+    # Whether to estimate affine background transformation
+    bg: True
+    # Whether to estimate the multi-resolution occlusion masks
+    multi_mask: True
+  generator_params:
+    # Number of features mutliplier
+    block_expansion: 64
+    # Maximum allowed number of features
+    max_features: 512
+    # Number of downsampling blocks and Upsampling blocks.
+    num_down_blocks: 3
+  dense_motion_params:
+    # Number of features mutliplier
+    block_expansion: 64
+    # Maximum allowed number of features
+    max_features: 1024
+    # Number of block in Unet. 
+    num_blocks: 5
+    # Optical flow is predicted on smaller images for better performance,
+    # scale_factor=0.25 means that 256x256 image will be resized to 64x64  
+    scale_factor: 0.25
+  avd_network_params:
+    # Bottleneck for identity branch
+    id_bottle_size: 128
+    # Bottleneck for pose branch
+    pose_bottle_size: 128
+
+# Parameters of training
+train_params:
+  # Number of training epochs 
+  num_epochs: 100
+  # For better i/o performance when number of videos is small number of epochs can be multiplied by this number.
+  # Thus effectivlly with num_repeats=100 each epoch is 100 times larger. 
+  num_repeats: 150
+  # Drop learning rate by 10 times after this epochs 
+  epoch_milestones: [70, 90]
+  # Initial learing rate for all modules
+  lr_generator: 2.0e-4
+  batch_size: 28
+  # Scales for perceptual pyramide loss. If scales = [1, 0.5, 0.25, 0.125] and image resolution is 256x256,
+  # than the loss will be computer on resolutions 256x256, 128x128, 64x64, 32x32.
+  scales: [1, 0.5, 0.25, 0.125]
+  # Dataset preprocessing cpu workers
+  dataloader_workers: 12
+  # Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs.
+  checkpoint_freq: 50
+  # Parameters of dropout
+  # The first dropout_epoch training uses dropout operation 
+  dropout_epoch: 35
+  # The probability P will linearly increase from dropout_startp to dropout_maxp in dropout_inc_epoch epochs
+  dropout_maxp: 0.7
+  dropout_startp: 0.0
+  dropout_inc_epoch: 10
+  # Estimate affine background transformation from the bg_start epoch.
+  bg_start: 0
+  # Parameters of random TPS transformation for equivariance loss
+  transform_params:
+    # Sigma for affine part
+    sigma_affine: 0.05
+    # Sigma for deformation part
+    sigma_tps: 0.005
+    # Number of point in the deformation grid
+    points_tps: 5
+  loss_weights:
+    # Weights for perceptual loss.
+    perceptual: [10, 10, 10, 10, 10]
+    # Weights for value equivariance.
+    equivariance_value: 10
+    # Weights for warp loss.
+    warp_loss: 10
+    # Weights for bg loss.
+    bg: 10
+
+# Parameters of training (animation-via-disentanglement)
+train_avd_params:
+  # Number of training epochs, visualization is produced after each epoch.
+  num_epochs: 100
+  # For better i/o performance when number of videos is small number of epochs can be multiplied by this number.
+  # Thus effectively with num_repeats=100 each epoch is 100 times larger.
+  num_repeats: 150
+  # Batch size.
+  batch_size: 256
+  # Save checkpoint this frequently. If checkpoint_freq=50, checkpoint will be saved every 50 epochs.
+  checkpoint_freq: 10
+  # Dataset preprocessing cpu workers
+  dataloader_workers: 24
+  # Drop learning rate 10 times after this epochs
+  epoch_milestones: [70, 90]
+  # Initial learning rate
+  lr: 1.0e-3
+  # Weights for equivariance loss.
+  lambda_shift: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

Thin-Plate-Spline-Motion-Model/config/ted-384.yaml

@@ -0,0 +1,73 @@
+dataset_params:
+  root_dir: ../TED384-v2
+  frame_shape: null
+  id_sampling: True
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    jitter_param:
+      brightness: 0.1
+      contrast: 0.1
+      saturation: 0.1
+      hue: 0.1
+
+model_params:
+  common_params:
+    num_tps: 10
+    num_channels: 3
+    bg: True
+    multi_mask: True
+  generator_params:
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 3
+  dense_motion_params:
+    block_expansion: 64
+    max_features: 1024
+    num_blocks: 5
+    scale_factor: 0.25
+  avd_network_params:
+    id_bottle_size: 128
+    pose_bottle_size: 128
+
+
+train_params:
+  num_epochs: 100
+  num_repeats: 150
+  epoch_milestones: [70, 90]
+  lr_generator: 2.0e-4
+  batch_size: 12
+  scales: [1, 0.5, 0.25, 0.125]
+  dataloader_workers: 6
+  checkpoint_freq: 50
+  dropout_epoch: 35
+  dropout_maxp: 0.5
+  dropout_startp: 0.0
+  dropout_inc_epoch: 10
+  bg_start: 0
+  transform_params:
+    sigma_affine: 0.05
+    sigma_tps: 0.005
+    points_tps: 5
+  loss_weights:
+    perceptual: [10, 10, 10, 10, 10]
+    equivariance_value: 10
+    warp_loss: 10
+    bg: 10
+
+train_avd_params:
+  num_epochs: 30
+  num_repeats: 500
+  batch_size: 256
+  dataloader_workers: 24
+  checkpoint_freq: 10
+  epoch_milestones: [20, 25]
+  lr: 1.0e-3
+  lambda_shift: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

Thin-Plate-Spline-Motion-Model/config/vox-256.yaml

@@ -0,0 +1,74 @@
+dataset_params:
+  root_dir: ../vox
+  frame_shape: null
+  id_sampling: True
+  augmentation_params:
+    flip_param:
+      horizontal_flip: True
+      time_flip: True
+    jitter_param:
+      brightness: 0.1
+      contrast: 0.1
+      saturation: 0.1
+      hue: 0.1
+
+
+model_params:
+  common_params:
+    num_tps: 10
+    num_channels: 3
+    bg: True
+    multi_mask: True
+  generator_params:
+    block_expansion: 64
+    max_features: 512
+    num_down_blocks: 3
+  dense_motion_params:
+    block_expansion: 64
+    max_features: 1024
+    num_blocks: 5
+    scale_factor: 0.25
+  avd_network_params:
+    id_bottle_size: 128
+    pose_bottle_size: 128
+
+
+train_params:
+  num_epochs: 100
+  num_repeats: 75
+  epoch_milestones: [70, 90]
+  lr_generator: 2.0e-4
+  batch_size: 28
+  scales: [1, 0.5, 0.25, 0.125]
+  dataloader_workers: 12
+  checkpoint_freq: 50
+  dropout_epoch: 35
+  dropout_maxp: 0.3
+  dropout_startp: 0.1
+  dropout_inc_epoch: 10
+  bg_start: 10
+  transform_params:
+    sigma_affine: 0.05
+    sigma_tps: 0.005
+    points_tps: 5
+  loss_weights:
+    perceptual: [10, 10, 10, 10, 10]
+    equivariance_value: 10
+    warp_loss: 10
+    bg: 10
+
+train_avd_params:
+  num_epochs: 200
+  num_repeats: 300
+  batch_size: 256
+  dataloader_workers: 24
+  checkpoint_freq: 50
+  epoch_milestones: [140, 180]
+  lr: 1.0e-3
+  lambda_shift: 1
+  random_scale: 0.25
+
+visualizer_params:
+  kp_size: 5
+  draw_border: True
+  colormap: 'gist_rainbow'

Thin-Plate-Spline-Motion-Model/demo.py

@@ -0,0 +1,179 @@
+import matplotlib
+matplotlib.use('Agg')
+import sys
+import yaml
+from argparse import ArgumentParser
+from tqdm import tqdm
+from scipy.spatial import ConvexHull
+import numpy as np
+import imageio
+from skimage.transform import resize
+from skimage import img_as_ubyte
+import torch
+from modules.inpainting_network import InpaintingNetwork
+from modules.keypoint_detector import KPDetector
+from modules.dense_motion import DenseMotionNetwork
+from modules.avd_network import AVDNetwork
+
+if sys.version_info[0] < 3:
+    raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
+
+def relative_kp(kp_source, kp_driving, kp_driving_initial):
+
+    source_area = ConvexHull(kp_source['fg_kp'][0].data.cpu().numpy()).volume
+    driving_area = ConvexHull(kp_driving_initial['fg_kp'][0].data.cpu().numpy()).volume
+    adapt_movement_scale = np.sqrt(source_area) / np.sqrt(driving_area)
+
+    kp_new = {k: v for k, v in kp_driving.items()}
+
+    kp_value_diff = (kp_driving['fg_kp'] - kp_driving_initial['fg_kp'])
+    kp_value_diff *= adapt_movement_scale
+    kp_new['fg_kp'] = kp_value_diff + kp_source['fg_kp']
+
+    return kp_new
+
+def load_checkpoints(config_path, checkpoint_path, device):
+    with open(config_path) as f:
+        config = yaml.full_load(f)
+
+    inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
+                                        **config['model_params']['common_params'])
+    kp_detector = KPDetector(**config['model_params']['common_params'])
+    dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
+                                              **config['model_params']['dense_motion_params'])
+    avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
+                             **config['model_params']['avd_network_params'])
+    kp_detector.to(device)
+    dense_motion_network.to(device)
+    inpainting.to(device)
+    avd_network.to(device)
+       
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+ 
+    inpainting.load_state_dict(checkpoint['inpainting_network'])
+    kp_detector.load_state_dict(checkpoint['kp_detector'])
+    dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
+    if 'avd_network' in checkpoint:
+        avd_network.load_state_dict(checkpoint['avd_network'])
+    
+    inpainting.eval()
+    kp_detector.eval()
+    dense_motion_network.eval()
+    avd_network.eval()
+    
+    return inpainting, kp_detector, dense_motion_network, avd_network
+
+
+def make_animation(source_image, driving_video, inpainting_network, kp_detector, dense_motion_network, avd_network, device, mode = 'relative'):
+    assert mode in ['standard', 'relative', 'avd']
+    with torch.no_grad():
+        predictions = []
+        source = torch.tensor(source_image[np.newaxis].astype(np.float32)).permute(0, 3, 1, 2)
+        source = source.to(device)
+        driving = torch.tensor(np.array(driving_video)[np.newaxis].astype(np.float32)).permute(0, 4, 1, 2, 3).to(device)
+        kp_source = kp_detector(source)
+        kp_driving_initial = kp_detector(driving[:, :, 0])
+
+        for frame_idx in tqdm(range(driving.shape[2])):
+            driving_frame = driving[:, :, frame_idx]
+            driving_frame = driving_frame.to(device)
+            kp_driving = kp_detector(driving_frame)
+            if mode == 'standard':
+                kp_norm = kp_driving
+            elif mode=='relative':
+                kp_norm = relative_kp(kp_source=kp_source, kp_driving=kp_driving,
+                                    kp_driving_initial=kp_driving_initial)
+            elif mode == 'avd':
+                kp_norm = avd_network(kp_source, kp_driving)
+            dense_motion = dense_motion_network(source_image=source, kp_driving=kp_norm,
+                                                    kp_source=kp_source, bg_param = None, 
+                                                    dropout_flag = False)
+            out = inpainting_network(source, dense_motion)
+
+            predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
+    return predictions
+
+
+def find_best_frame(source, driving, cpu):
+    import face_alignment
+
+    def normalize_kp(kp):
+        kp = kp - kp.mean(axis=0, keepdims=True)
+        area = ConvexHull(kp[:, :2]).volume
+        area = np.sqrt(area)
+        kp[:, :2] = kp[:, :2] / area
+        return kp
+
+    fa = face_alignment.FaceAlignment(face_alignment.LandmarksType._2D, flip_input=True,
+                                      device= 'cpu' if cpu else 'cuda')
+    kp_source = fa.get_landmarks(255 * source)[0]
+    kp_source = normalize_kp(kp_source)
+    norm  = float('inf')
+    frame_num = 0
+    for i, image in tqdm(enumerate(driving)):
+        try:
+            kp_driving = fa.get_landmarks(255 * image)[0]
+            kp_driving = normalize_kp(kp_driving)
+            new_norm = (np.abs(kp_source - kp_driving) ** 2).sum()
+            if new_norm < norm:
+                norm = new_norm
+                frame_num = i
+        except:
+            pass
+    return frame_num
+
+
+if __name__ == "__main__":
+    parser = ArgumentParser()
+    parser.add_argument("--config", required=True, help="path to config")
+    parser.add_argument("--checkpoint", default='checkpoints/vox.pth.tar', help="path to checkpoint to restore")
+
+    parser.add_argument("--source_image", default='./assets/source.png', help="path to source image")
+    parser.add_argument("--driving_video", default='./assets/driving.mp4', help="path to driving video")
+    parser.add_argument("--result_video", default='./result.mp4', help="path to output")
+    
+    parser.add_argument("--img_shape", default="256,256", type=lambda x: list(map(int, x.split(','))),
+                        help='Shape of image, that the model was trained on.')
+    
+    parser.add_argument("--mode", default='relative', choices=['standard', 'relative', 'avd'], help="Animate mode: ['standard', 'relative', 'avd'], when use the relative mode to animate a face, use '--find_best_frame' can get better quality result")
+    
+    parser.add_argument("--find_best_frame", dest="find_best_frame", action="store_true", 
+                        help="Generate from the frame that is the most alligned with source. (Only for faces, requires face_aligment lib)")
+
+    parser.add_argument("--cpu", dest="cpu", action="store_true", help="cpu mode.")
+
+    opt = parser.parse_args()
+
+    source_image = imageio.imread(opt.source_image)
+    reader = imageio.get_reader(opt.driving_video)
+    fps = reader.get_meta_data()['fps']
+    driving_video = []
+    try:
+        for im in reader:
+            driving_video.append(im)
+    except RuntimeError:
+        pass
+    reader.close()
+    
+    if opt.cpu:
+        device = torch.device('cpu')
+    else:
+        device = torch.device('cuda')
+    
+    source_image = resize(source_image, opt.img_shape)[..., :3]
+    driving_video = [resize(frame, opt.img_shape)[..., :3] for frame in driving_video]
+    inpainting, kp_detector, dense_motion_network, avd_network = load_checkpoints(config_path = opt.config, checkpoint_path = opt.checkpoint, device = device)
+ 
+    if opt.find_best_frame:
+        i = find_best_frame(source_image, driving_video, opt.cpu)
+        print ("Best frame: " + str(i))
+        driving_forward = driving_video[i:]
+        driving_backward = driving_video[:(i+1)][::-1]
+        predictions_forward = make_animation(source_image, driving_forward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
+        predictions_backward = make_animation(source_image, driving_backward, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
+        predictions = predictions_backward[::-1] + predictions_forward[1:]
+    else:
+        predictions = make_animation(source_image, driving_video, inpainting, kp_detector, dense_motion_network, avd_network, device = device, mode = opt.mode)
+    
+    imageio.mimsave(opt.result_video, [img_as_ubyte(frame) for frame in predictions], fps=fps)
+

Thin-Plate-Spline-Motion-Model/frames_dataset.py

@@ -0,0 +1,173 @@
+import os
+from skimage import io, img_as_float32
+from skimage.color import gray2rgb
+from sklearn.model_selection import train_test_split
+from imageio import mimread
+from skimage.transform import resize
+import numpy as np
+from torch.utils.data import Dataset
+from augmentation import AllAugmentationTransform
+import glob
+from functools import partial
+
+
+def read_video(name, frame_shape):
+    """
+    Read video which can be:
+      - an image of concatenated frames
+      - '.mp4' and'.gif'
+      - folder with videos
+    """
+
+    if os.path.isdir(name):
+        frames = sorted(os.listdir(name))
+        num_frames = len(frames)
+        video_array = np.array(
+            [img_as_float32(io.imread(os.path.join(name, frames[idx]))) for idx in range(num_frames)])
+    elif name.lower().endswith('.png') or name.lower().endswith('.jpg'):
+        image = io.imread(name)
+
+        if len(image.shape) == 2 or image.shape[2] == 1:
+            image = gray2rgb(image)
+
+        if image.shape[2] == 4:
+            image = image[..., :3]
+
+        image = img_as_float32(image)
+
+        video_array = np.moveaxis(image, 1, 0)
+
+        video_array = video_array.reshape((-1,) + frame_shape)
+        video_array = np.moveaxis(video_array, 1, 2)
+    elif name.lower().endswith('.gif') or name.lower().endswith('.mp4') or name.lower().endswith('.mov'):
+        video = mimread(name)
+        if len(video[0].shape) == 2:
+            video = [gray2rgb(frame) for frame in video]
+        if frame_shape is not None:
+            video = np.array([resize(frame, frame_shape) for frame in video])
+        video = np.array(video)
+        if video.shape[-1] == 4:
+            video = video[..., :3]
+        video_array = img_as_float32(video)
+    else:
+        raise Exception("Unknown file extensions  %s" % name)
+
+    return video_array
+
+
+class FramesDataset(Dataset):
+    """
+    Dataset of videos, each video can be represented as:
+      - an image of concatenated frames
+      - '.mp4' or '.gif'
+      - folder with all frames
+    """
+
+    def __init__(self, root_dir, frame_shape=(256, 256, 3), id_sampling=False, is_train=True,
+                 random_seed=0, pairs_list=None, augmentation_params=None):
+        self.root_dir = root_dir
+        self.videos = os.listdir(root_dir)
+        self.frame_shape = frame_shape
+        print(self.frame_shape)
+        self.pairs_list = pairs_list
+        self.id_sampling = id_sampling
+
+        if os.path.exists(os.path.join(root_dir, 'train')):
+            assert os.path.exists(os.path.join(root_dir, 'test'))
+            print("Use predefined train-test split.")
+            if id_sampling:
+                train_videos = {os.path.basename(video).split('#')[0] for video in
+                                os.listdir(os.path.join(root_dir, 'train'))}
+                train_videos = list(train_videos)
+            else:
+                train_videos = os.listdir(os.path.join(root_dir, 'train'))
+            test_videos = os.listdir(os.path.join(root_dir, 'test'))
+            self.root_dir = os.path.join(self.root_dir, 'train' if is_train else 'test')
+        else:
+            print("Use random train-test split.")
+            train_videos, test_videos = train_test_split(self.videos, random_state=random_seed, test_size=0.2)
+
+        if is_train:
+            self.videos = train_videos
+        else:
+            self.videos = test_videos
+
+        self.is_train = is_train
+
+        if self.is_train:
+            self.transform = AllAugmentationTransform(**augmentation_params)
+        else:
+            self.transform = None
+
+    def __len__(self):
+        return len(self.videos)
+
+    def __getitem__(self, idx):
+            
+        if self.is_train and self.id_sampling:   
+            name = self.videos[idx]
+            path = np.random.choice(glob.glob(os.path.join(self.root_dir, name + '*.mp4')))
+        else:
+            name = self.videos[idx]
+            path = os.path.join(self.root_dir, name)
+
+        video_name = os.path.basename(path)
+        if self.is_train and os.path.isdir(path):
+
+            frames = os.listdir(path)
+            num_frames = len(frames)
+            frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2))
+            
+            if self.frame_shape is not None:
+                resize_fn = partial(resize, output_shape=self.frame_shape)
+            else:
+                resize_fn = img_as_float32
+
+            if type(frames[0]) is bytes:
+                video_array = [resize_fn(io.imread(os.path.join(path, frames[idx].decode('utf-8')))) for idx in
+                            frame_idx]
+            else:
+                video_array = [resize_fn(io.imread(os.path.join(path, frames[idx]))) for idx in frame_idx]
+        else:
+                 
+            video_array = read_video(path, frame_shape=self.frame_shape)
+            
+            num_frames = len(video_array)
+            frame_idx = np.sort(np.random.choice(num_frames, replace=True, size=2)) if self.is_train else range(
+                num_frames)
+            video_array = video_array[frame_idx]
+            
+
+        if self.transform is not None:
+            video_array = self.transform(video_array)
+
+        out = {}
+        if self.is_train:
+            source = np.array(video_array[0], dtype='float32')
+            driving = np.array(video_array[1], dtype='float32')
+
+            out['driving'] = driving.transpose((2, 0, 1))
+            out['source'] = source.transpose((2, 0, 1))
+        else:
+            video = np.array(video_array, dtype='float32')
+            out['video'] = video.transpose((3, 0, 1, 2))
+
+        out['name'] = video_name
+        return out
+
+
+class DatasetRepeater(Dataset):
+    """
+    Pass several times over the same dataset for better i/o performance
+    """
+
+    def __init__(self, dataset, num_repeats=100):
+        self.dataset = dataset
+        self.num_repeats = num_repeats
+
+    def __len__(self):
+        return self.num_repeats * self.dataset.__len__()
+
+    def __getitem__(self, idx):
+        return self.dataset[idx % self.dataset.__len__()]
+

Thin-Plate-Spline-Motion-Model/logger.py

@@ -0,0 +1,212 @@
+import numpy as np
+import torch
+import torch.nn.functional as F
+import imageio
+
+import os
+from skimage.draw import circle
+
+import matplotlib.pyplot as plt
+import collections
+
+
+class Logger:
+    def __init__(self, log_dir, checkpoint_freq=50, visualizer_params=None, zfill_num=8, log_file_name='log.txt'):
+
+        self.loss_list = []
+        self.cpk_dir = log_dir
+        self.visualizations_dir = os.path.join(log_dir, 'train-vis')
+        if not os.path.exists(self.visualizations_dir):
+            os.makedirs(self.visualizations_dir)
+        self.log_file = open(os.path.join(log_dir, log_file_name), 'a')
+        self.zfill_num = zfill_num
+        self.visualizer = Visualizer(**visualizer_params)
+        self.checkpoint_freq = checkpoint_freq
+        self.epoch = 0
+        self.best_loss = float('inf')
+        self.names = None
+
+    def log_scores(self, loss_names):
+        loss_mean = np.array(self.loss_list).mean(axis=0)
+
+        loss_string = "; ".join(["%s - %.5f" % (name, value) for name, value in zip(loss_names, loss_mean)])
+        loss_string = str(self.epoch).zfill(self.zfill_num) + ") " + loss_string
+
+        print(loss_string, file=self.log_file)
+        self.loss_list = []
+        self.log_file.flush()
+
+    def visualize_rec(self, inp, out):
+        image = self.visualizer.visualize(inp['driving'], inp['source'], out)
+        imageio.imsave(os.path.join(self.visualizations_dir, "%s-rec.png" % str(self.epoch).zfill(self.zfill_num)), image)
+
+    def save_cpk(self, emergent=False):
+        cpk = {k: v.state_dict() for k, v in self.models.items()}
+        cpk['epoch'] = self.epoch
+        cpk_path = os.path.join(self.cpk_dir, '%s-checkpoint.pth.tar' % str(self.epoch).zfill(self.zfill_num)) 
+        if not (os.path.exists(cpk_path) and emergent):
+            torch.save(cpk, cpk_path)
+
+    @staticmethod
+    def load_cpk(checkpoint_path, inpainting_network=None, dense_motion_network =None, kp_detector=None, 
+                bg_predictor=None, avd_network=None, optimizer=None, optimizer_bg_predictor=None,
+                optimizer_avd=None):
+        checkpoint = torch.load(checkpoint_path)
+        if inpainting_network is not None:
+            inpainting_network.load_state_dict(checkpoint['inpainting_network'])
+        if kp_detector is not None:
+            kp_detector.load_state_dict(checkpoint['kp_detector'])
+        if bg_predictor is not None and 'bg_predictor' in checkpoint:
+            bg_predictor.load_state_dict(checkpoint['bg_predictor'])
+        if dense_motion_network is not None:
+            dense_motion_network.load_state_dict(checkpoint['dense_motion_network'])
+        if avd_network is not None:
+            if 'avd_network' in checkpoint:
+                avd_network.load_state_dict(checkpoint['avd_network'])
+        if optimizer_bg_predictor is not None and 'optimizer_bg_predictor' in checkpoint:
+            optimizer_bg_predictor.load_state_dict(checkpoint['optimizer_bg_predictor'])
+        if optimizer is not None and 'optimizer' in checkpoint:
+            optimizer.load_state_dict(checkpoint['optimizer'])
+        if optimizer_avd is not None:
+            if 'optimizer_avd' in checkpoint:
+                optimizer_avd.load_state_dict(checkpoint['optimizer_avd'])
+        epoch = -1
+        if 'epoch' in checkpoint:
+            epoch = checkpoint['epoch']
+        return epoch
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, exc_type, exc_value, tb):
+        if 'models' in self.__dict__:
+            self.save_cpk()
+        self.log_file.close()
+
+    def log_iter(self, losses):
+        losses = collections.OrderedDict(losses.items())
+        self.names = list(losses.keys())
+        self.loss_list.append(list(losses.values()))
+
+    def log_epoch(self, epoch, models, inp, out):
+        self.epoch = epoch
+        self.models = models
+        if (self.epoch + 1) % self.checkpoint_freq == 0:
+            self.save_cpk()
+        self.log_scores(self.names)
+        self.visualize_rec(inp, out)
+
+
+class Visualizer:
+    def __init__(self, kp_size=5, draw_border=False, colormap='gist_rainbow'):
+        self.kp_size = kp_size
+        self.draw_border = draw_border
+        self.colormap = plt.get_cmap(colormap)
+
+    def draw_image_with_kp(self, image, kp_array):
+        image = np.copy(image)
+        spatial_size = np.array(image.shape[:2][::-1])[np.newaxis]
+        kp_array = spatial_size * (kp_array + 1) / 2
+        num_kp = kp_array.shape[0]
+        for kp_ind, kp in enumerate(kp_array):
+            rr, cc = circle(kp[1], kp[0], self.kp_size, shape=image.shape[:2])
+            image[rr, cc] = np.array(self.colormap(kp_ind / num_kp))[:3]
+        return image
+
+    def create_image_column_with_kp(self, images, kp):
+        image_array = np.array([self.draw_image_with_kp(v, k) for v, k in zip(images, kp)])
+        return self.create_image_column(image_array)
+
+    def create_image_column(self, images):
+        if self.draw_border:
+            images = np.copy(images)
+            images[:, :, [0, -1]] = (1, 1, 1)
+            images[:, :, [0, -1]] = (1, 1, 1)
+        return np.concatenate(list(images), axis=0)
+
+    def create_image_grid(self, *args):
+        out = []
+        for arg in args:
+            if type(arg) == tuple:
+                out.append(self.create_image_column_with_kp(arg[0], arg[1]))
+            else:
+                out.append(self.create_image_column(arg))
+        return np.concatenate(out, axis=1)
+
+    def visualize(self, driving, source, out):
+        images = []
+
+        # Source image with keypoints
+        source = source.data.cpu()
+        kp_source = out['kp_source']['fg_kp'].data.cpu().numpy()
+        source = np.transpose(source, [0, 2, 3, 1])
+        images.append((source, kp_source))
+
+        # Equivariance visualization
+        if 'transformed_frame' in out:
+            transformed = out['transformed_frame'].data.cpu().numpy()
+            transformed = np.transpose(transformed, [0, 2, 3, 1])
+            transformed_kp = out['transformed_kp']['fg_kp'].data.cpu().numpy()
+            images.append((transformed, transformed_kp))
+
+        # Driving image with keypoints
+        kp_driving = out['kp_driving']['fg_kp'].data.cpu().numpy()
+        driving = driving.data.cpu().numpy()
+        driving = np.transpose(driving, [0, 2, 3, 1])
+        images.append((driving, kp_driving))
+
+        # Deformed image
+        if 'deformed' in out:
+            deformed = out['deformed'].data.cpu().numpy()
+            deformed = np.transpose(deformed, [0, 2, 3, 1])
+            images.append(deformed)
+
+        # Result with and without keypoints
+        prediction = out['prediction'].data.cpu().numpy()
+        prediction = np.transpose(prediction, [0, 2, 3, 1])
+        if 'kp_norm' in out:
+            kp_norm = out['kp_norm']['fg_kp'].data.cpu().numpy()
+            images.append((prediction, kp_norm))
+        images.append(prediction)
+
+
+        ## Occlusion map
+        if 'occlusion_map' in out:
+            for i in range(len(out['occlusion_map'])):
+                occlusion_map = out['occlusion_map'][i].data.cpu().repeat(1, 3, 1, 1)
+                occlusion_map = F.interpolate(occlusion_map, size=source.shape[1:3]).numpy()
+                occlusion_map = np.transpose(occlusion_map, [0, 2, 3, 1])
+                images.append(occlusion_map)
+
+        # Deformed images according to each individual transform
+        if 'deformed_source' in out:
+            full_mask = []
+            for i in range(out['deformed_source'].shape[1]):
+                image = out['deformed_source'][:, i].data.cpu()
+                # import ipdb;ipdb.set_trace()
+                image = F.interpolate(image, size=source.shape[1:3])
+                mask = out['contribution_maps'][:, i:(i+1)].data.cpu().repeat(1, 3, 1, 1)
+                mask = F.interpolate(mask, size=source.shape[1:3])
+                image = np.transpose(image.numpy(), (0, 2, 3, 1))
+                mask = np.transpose(mask.numpy(), (0, 2, 3, 1))
+
+                if i != 0:
+                    color = np.array(self.colormap((i - 1) / (out['deformed_source'].shape[1] - 1)))[:3]
+                else:
+                    color = np.array((0, 0, 0))
+
+                color = color.reshape((1, 1, 1, 3))
+
+                images.append(image)
+                if i != 0:
+                    images.append(mask * color)
+                else:
+                    images.append(mask)
+
+                full_mask.append(mask * color)
+
+            images.append(sum(full_mask))
+
+        image = self.create_image_grid(*images)
+        image = (255 * image).astype(np.uint8)
+        return image

Thin-Plate-Spline-Motion-Model/modules/avd_network.py

@@ -0,0 +1,65 @@
+
+import torch
+from torch import nn
+
+
+class AVDNetwork(nn.Module):
+    """
+    Animation via Disentanglement network
+    """
+
+    def __init__(self, num_tps, id_bottle_size=64, pose_bottle_size=64):
+        super(AVDNetwork, self).__init__()
+        input_size = 5*2 * num_tps
+        self.num_tps = num_tps
+
+        self.id_encoder = nn.Sequential(
+            nn.Linear(input_size, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(inplace=True),
+            nn.Linear(512, 1024),
+            nn.BatchNorm1d(1024),
+            nn.ReLU(inplace=True),
+            nn.Linear(1024, id_bottle_size)
+        )
+
+        self.pose_encoder = nn.Sequential(
+            nn.Linear(input_size, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(inplace=True),
+            nn.Linear(256, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(inplace=True),
+            nn.Linear(512, 1024),
+            nn.BatchNorm1d(1024),
+            nn.ReLU(inplace=True),
+            nn.Linear(1024, pose_bottle_size)
+        )
+
+        self.decoder = nn.Sequential(
+            nn.Linear(pose_bottle_size + id_bottle_size, 1024),
+            nn.BatchNorm1d(1024),
+            nn.ReLU(),
+            nn.Linear(1024, 512),
+            nn.BatchNorm1d(512),
+            nn.ReLU(),
+            nn.Linear(512, 256),
+            nn.BatchNorm1d(256),
+            nn.ReLU(),
+            nn.Linear(256, input_size)
+        )
+
+    def forward(self, kp_source, kp_random):
+
+        bs = kp_source['fg_kp'].shape[0]
+        
+        pose_emb = self.pose_encoder(kp_random['fg_kp'].view(bs, -1))
+        id_emb = self.id_encoder(kp_source['fg_kp'].view(bs, -1))
+
+        rec = self.decoder(torch.cat([pose_emb, id_emb], dim=1))
+
+        rec =  {'fg_kp': rec.view(bs, self.num_tps*5, -1)}
+        return rec

Thin-Plate-Spline-Motion-Model/modules/bg_motion_predictor.py

@@ -0,0 +1,24 @@
+from torch import nn
+import torch
+from torchvision import models
+
+class BGMotionPredictor(nn.Module):
+    """
+    Module for background estimation, return single transformation, parametrized as 3x3 matrix. The third row is [0 0 1]
+    """
+
+    def __init__(self):
+        super(BGMotionPredictor, self).__init__()
+        self.bg_encoder = models.resnet18(pretrained=False)
+        self.bg_encoder.conv1 = nn.Conv2d(6, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)
+        num_features = self.bg_encoder.fc.in_features
+        self.bg_encoder.fc = nn.Linear(num_features, 6)
+        self.bg_encoder.fc.weight.data.zero_()
+        self.bg_encoder.fc.bias.data.copy_(torch.tensor([1, 0, 0, 0, 1, 0], dtype=torch.float))
+
+    def forward(self, source_image, driving_image):
+        bs = source_image.shape[0]
+        out = torch.eye(3).unsqueeze(0).repeat(bs, 1, 1).type(source_image.type())
+        prediction = self.bg_encoder(torch.cat([source_image, driving_image], dim=1))
+        out[:, :2, :] = prediction.view(bs, 2, 3)
+        return out

Thin-Plate-Spline-Motion-Model/modules/dense_motion.py

@@ -0,0 +1,164 @@
+from torch import nn
+import torch.nn.functional as F
+import torch
+from modules.util import Hourglass, AntiAliasInterpolation2d, make_coordinate_grid, kp2gaussian
+from modules.util import to_homogeneous, from_homogeneous, UpBlock2d, TPS
+import math
+
+class DenseMotionNetwork(nn.Module):
+    """
+    Module that estimating an optical flow and multi-resolution occlusion masks 
+                        from K TPS transformations and an affine transformation.
+    """
+
+    def __init__(self, block_expansion, num_blocks, max_features, num_tps, num_channels, 
+                 scale_factor=0.25, bg = False, multi_mask = True, kp_variance=0.01):
+        super(DenseMotionNetwork, self).__init__()
+
+        if scale_factor != 1:
+            self.down = AntiAliasInterpolation2d(num_channels, scale_factor)
+        self.scale_factor = scale_factor
+        self.multi_mask = multi_mask
+
+        self.hourglass = Hourglass(block_expansion=block_expansion, in_features=(num_channels * (num_tps+1) + num_tps*5+1),
+                                   max_features=max_features, num_blocks=num_blocks)
+
+        hourglass_output_size = self.hourglass.out_channels
+        self.maps = nn.Conv2d(hourglass_output_size[-1], num_tps + 1, kernel_size=(7, 7), padding=(3, 3))
+
+        if multi_mask:
+            up = []
+            self.up_nums = int(math.log(1/scale_factor, 2))
+            self.occlusion_num = 4
+            
+            channel = [hourglass_output_size[-1]//(2**i) for i in range(self.up_nums)]
+            for i in range(self.up_nums):
+                up.append(UpBlock2d(channel[i], channel[i]//2, kernel_size=3, padding=1))
+            self.up = nn.ModuleList(up)
+
+            channel = [hourglass_output_size[-i-1] for i in range(self.occlusion_num-self.up_nums)[::-1]]
+            for i in range(self.up_nums):
+                channel.append(hourglass_output_size[-1]//(2**(i+1)))
+            occlusion = []
+            
+            for i in range(self.occlusion_num):
+                occlusion.append(nn.Conv2d(channel[i], 1, kernel_size=(7, 7), padding=(3, 3)))
+            self.occlusion = nn.ModuleList(occlusion)
+        else:
+            occlusion = [nn.Conv2d(hourglass_output_size[-1], 1, kernel_size=(7, 7), padding=(3, 3))]
+            self.occlusion = nn.ModuleList(occlusion)
+
+        self.num_tps = num_tps
+        self.bg = bg
+        self.kp_variance = kp_variance
+
+        
+    def create_heatmap_representations(self, source_image, kp_driving, kp_source):
+
+        spatial_size = source_image.shape[2:]
+        gaussian_driving = kp2gaussian(kp_driving['fg_kp'], spatial_size=spatial_size, kp_variance=self.kp_variance)
+        gaussian_source = kp2gaussian(kp_source['fg_kp'], spatial_size=spatial_size, kp_variance=self.kp_variance)
+        heatmap = gaussian_driving - gaussian_source
+
+        zeros = torch.zeros(heatmap.shape[0], 1, spatial_size[0], spatial_size[1]).type(heatmap.type()).to(heatmap.device)
+        heatmap = torch.cat([zeros, heatmap], dim=1)
+
+        return heatmap
+
+    def create_transformations(self, source_image, kp_driving, kp_source, bg_param):
+        # K TPS transformaions
+        bs, _, h, w = source_image.shape
+        kp_1 = kp_driving['fg_kp']
+        kp_2 = kp_source['fg_kp']
+        kp_1 = kp_1.view(bs, -1, 5, 2)
+        kp_2 = kp_2.view(bs, -1, 5, 2)
+        trans = TPS(mode = 'kp', bs = bs, kp_1 = kp_1, kp_2 = kp_2)
+        driving_to_source = trans.transform_frame(source_image)
+
+        identity_grid = make_coordinate_grid((h, w), type=kp_1.type()).to(kp_1.device)
+        identity_grid = identity_grid.view(1, 1, h, w, 2)
+        identity_grid = identity_grid.repeat(bs, 1, 1, 1, 1)
+
+        # affine background transformation
+        if not (bg_param is None):            
+            identity_grid = to_homogeneous(identity_grid)
+            identity_grid = torch.matmul(bg_param.view(bs, 1, 1, 1, 3, 3), identity_grid.unsqueeze(-1)).squeeze(-1)
+            identity_grid = from_homogeneous(identity_grid)
+
+        transformations = torch.cat([identity_grid, driving_to_source], dim=1)
+        return transformations
+
+    def create_deformed_source_image(self, source_image, transformations):
+
+        bs, _, h, w = source_image.shape
+        source_repeat = source_image.unsqueeze(1).unsqueeze(1).repeat(1, self.num_tps + 1, 1, 1, 1, 1)
+        source_repeat = source_repeat.view(bs * (self.num_tps + 1), -1, h, w)
+        transformations = transformations.view((bs * (self.num_tps + 1), h, w, -1))
+        deformed = F.grid_sample(source_repeat, transformations, align_corners=True)
+        deformed = deformed.view((bs, self.num_tps+1, -1, h, w))
+        return deformed
+
+    def dropout_softmax(self, X, P):
+        '''
+        Dropout for TPS transformations. Eq(7) and Eq(8) in the paper.
+        '''
+        drop = (torch.rand(X.shape[0],X.shape[1]) < (1-P)).type(X.type()).to(X.device)
+        drop[..., 0] = 1
+        drop = drop.repeat(X.shape[2],X.shape[3],1,1).permute(2,3,0,1)
+
+        maxx = X.max(1).values.unsqueeze_(1)
+        X = X - maxx
+        X_exp = X.exp()
+        X[:,1:,...] /= (1-P)
+        mask_bool =(drop == 0)
+        X_exp = X_exp.masked_fill(mask_bool, 0)
+        partition = X_exp.sum(dim=1, keepdim=True) + 1e-6
+        return X_exp / partition  
+
+    def forward(self, source_image, kp_driving, kp_source, bg_param = None, dropout_flag=False, dropout_p = 0):
+        if self.scale_factor != 1:
+            source_image = self.down(source_image)
+
+        bs, _, h, w = source_image.shape
+
+        out_dict = dict()
+        heatmap_representation = self.create_heatmap_representations(source_image, kp_driving, kp_source)
+        transformations = self.create_transformations(source_image, kp_driving, kp_source, bg_param)
+        deformed_source = self.create_deformed_source_image(source_image, transformations)
+        out_dict['deformed_source'] = deformed_source
+        # out_dict['transformations'] = transformations
+        deformed_source = deformed_source.view(bs,-1,h,w)
+        input = torch.cat([heatmap_representation, deformed_source], dim=1)
+        input = input.view(bs, -1, h, w)
+
+        prediction = self.hourglass(input, mode = 1)
+
+        contribution_maps = self.maps(prediction[-1]) 
+        if(dropout_flag):
+            contribution_maps = self.dropout_softmax(contribution_maps, dropout_p)
+        else:
+            contribution_maps = F.softmax(contribution_maps, dim=1)
+        out_dict['contribution_maps'] = contribution_maps
+
+        # Combine the K+1 transformations
+        # Eq(6) in the paper
+        contribution_maps = contribution_maps.unsqueeze(2)
+        transformations = transformations.permute(0, 1, 4, 2, 3)
+        deformation = (transformations * contribution_maps).sum(dim=1)
+        deformation = deformation.permute(0, 2, 3, 1)
+
+        out_dict['deformation'] = deformation # Optical Flow
+
+        occlusion_map = []
+        if self.multi_mask:
+            for i in range(self.occlusion_num-self.up_nums):
+                occlusion_map.append(torch.sigmoid(self.occlusion[i](prediction[self.up_nums-self.occlusion_num+i])))
+            prediction = prediction[-1]
+            for i in range(self.up_nums):
+                prediction = self.up[i](prediction)
+                occlusion_map.append(torch.sigmoid(self.occlusion[i+self.occlusion_num-self.up_nums](prediction)))
+        else:
+            occlusion_map.append(torch.sigmoid(self.occlusion[0](prediction[-1])))
+                
+        out_dict['occlusion_map'] = occlusion_map # Multi-resolution Occlusion Masks
+        return out_dict

Thin-Plate-Spline-Motion-Model/modules/inpainting_network.py

@@ -0,0 +1,127 @@
+import torch
+from torch import nn
+import torch.nn.functional as F
+from modules.util import ResBlock2d, SameBlock2d, UpBlock2d, DownBlock2d
+from modules.dense_motion import DenseMotionNetwork
+
+
+class InpaintingNetwork(nn.Module):
+    """
+    Inpaint the missing regions and reconstruct the Driving image.
+    """
+    def __init__(self, num_channels, block_expansion, max_features, num_down_blocks, multi_mask = True, **kwargs):
+        super(InpaintingNetwork, self).__init__()
+
+        self.num_down_blocks = num_down_blocks
+        self.multi_mask = multi_mask
+        self.first = SameBlock2d(num_channels, block_expansion, kernel_size=(7, 7), padding=(3, 3))
+
+        down_blocks = []
+        up_blocks = []
+        resblock = []
+        for i in range(num_down_blocks):
+            in_features = min(max_features, block_expansion * (2 ** i))
+            out_features = min(max_features, block_expansion * (2 ** (i + 1)))
+            down_blocks.append(DownBlock2d(in_features, out_features, kernel_size=(3, 3), padding=(1, 1)))
+            decoder_in_feature = out_features * 2
+            if i==num_down_blocks-1:
+                decoder_in_feature = out_features
+            up_blocks.append(UpBlock2d(decoder_in_feature, in_features, kernel_size=(3, 3), padding=(1, 1)))
+            resblock.append(ResBlock2d(decoder_in_feature, kernel_size=(3, 3), padding=(1, 1)))
+            resblock.append(ResBlock2d(decoder_in_feature, kernel_size=(3, 3), padding=(1, 1)))
+        self.down_blocks = nn.ModuleList(down_blocks)
+        self.up_blocks = nn.ModuleList(up_blocks[::-1])
+        self.resblock = nn.ModuleList(resblock[::-1])
+
+        self.final = nn.Conv2d(block_expansion, num_channels, kernel_size=(7, 7), padding=(3, 3))
+        self.num_channels = num_channels
+
+    def deform_input(self, inp, deformation):
+        _, h_old, w_old, _ = deformation.shape
+        _, _, h, w = inp.shape
+        if h_old != h or w_old != w:
+            deformation = deformation.permute(0, 3, 1, 2)
+            deformation = F.interpolate(deformation, size=(h, w), mode='bilinear', align_corners=True)
+            deformation = deformation.permute(0, 2, 3, 1)
+        return F.grid_sample(inp, deformation,align_corners=True)
+
+    def occlude_input(self, inp, occlusion_map):
+        if not self.multi_mask:
+            if inp.shape[2] != occlusion_map.shape[2] or inp.shape[3] != occlusion_map.shape[3]:
+                occlusion_map = F.interpolate(occlusion_map, size=inp.shape[2:], mode='bilinear',align_corners=True)
+        out = inp * occlusion_map
+        return out
+
+    def forward(self, source_image, dense_motion):
+        out = self.first(source_image) 
+        encoder_map = [out]
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out)
+            encoder_map.append(out)
+
+        output_dict = {}
+        output_dict['contribution_maps'] = dense_motion['contribution_maps']
+        output_dict['deformed_source'] = dense_motion['deformed_source']
+
+        occlusion_map = dense_motion['occlusion_map']
+        output_dict['occlusion_map'] = occlusion_map
+
+        deformation = dense_motion['deformation']
+        out_ij = self.deform_input(out.detach(), deformation)
+        out = self.deform_input(out, deformation)
+
+        out_ij = self.occlude_input(out_ij, occlusion_map[0].detach())
+        out = self.occlude_input(out, occlusion_map[0])
+
+        warped_encoder_maps = []
+        warped_encoder_maps.append(out_ij)
+
+        for i in range(self.num_down_blocks):
+            
+            out = self.resblock[2*i](out)
+            out = self.resblock[2*i+1](out)
+            out = self.up_blocks[i](out)
+            
+            encode_i = encoder_map[-(i+2)]
+            encode_ij = self.deform_input(encode_i.detach(), deformation)
+            encode_i = self.deform_input(encode_i, deformation)
+            
+            occlusion_ind = 0
+            if self.multi_mask:
+                occlusion_ind = i+1
+            encode_ij = self.occlude_input(encode_ij, occlusion_map[occlusion_ind].detach())
+            encode_i = self.occlude_input(encode_i, occlusion_map[occlusion_ind])
+            warped_encoder_maps.append(encode_ij)
+
+            if(i==self.num_down_blocks-1):
+                break
+
+            out = torch.cat([out, encode_i], 1)
+
+        deformed_source = self.deform_input(source_image, deformation)
+        output_dict["deformed"] = deformed_source
+        output_dict["warped_encoder_maps"] = warped_encoder_maps
+
+        occlusion_last = occlusion_map[-1]
+        if not self.multi_mask:
+            occlusion_last = F.interpolate(occlusion_last, size=out.shape[2:], mode='bilinear',align_corners=True)
+
+        out = out * (1 - occlusion_last) + encode_i
+        out = self.final(out)
+        out = torch.sigmoid(out)
+        out = out * (1 - occlusion_last) + deformed_source * occlusion_last
+        output_dict["prediction"] = out
+
+        return output_dict
+
+    def get_encode(self, driver_image, occlusion_map):
+        out = self.first(driver_image)
+        encoder_map = []
+        encoder_map.append(self.occlude_input(out.detach(), occlusion_map[-1].detach()))
+        for i in range(len(self.down_blocks)):
+            out = self.down_blocks[i](out.detach())
+            out_mask = self.occlude_input(out.detach(), occlusion_map[2-i].detach())
+            encoder_map.append(out_mask.detach())
+
+        return encoder_map
+

Thin-Plate-Spline-Motion-Model/modules/keypoint_detector.py

@@ -0,0 +1,27 @@
+from torch import nn
+import torch
+from torchvision import models
+
+class KPDetector(nn.Module):
+    """
+    Predict K*5 keypoints.
+    """
+
+    def __init__(self, num_tps, **kwargs):
+        super(KPDetector, self).__init__()
+        self.num_tps = num_tps
+
+        self.fg_encoder = models.resnet18(pretrained=False)
+        num_features = self.fg_encoder.fc.in_features
+        self.fg_encoder.fc = nn.Linear(num_features, num_tps*5*2)
+
+        
+    def forward(self, image):
+
+        fg_kp = self.fg_encoder(image)
+        bs, _, = fg_kp.shape
+        fg_kp = torch.sigmoid(fg_kp)
+        fg_kp = fg_kp * 2 - 1
+        out = {'fg_kp': fg_kp.view(bs, self.num_tps*5, -1)}
+
+        return out

Thin-Plate-Spline-Motion-Model/modules/model.py

@@ -0,0 +1,182 @@
+from torch import nn
+import torch
+import torch.nn.functional as F
+from modules.util import AntiAliasInterpolation2d, TPS
+from torchvision import models
+import numpy as np
+
+
+class Vgg19(torch.nn.Module):
+    """
+    Vgg19 network for perceptual loss. See Sec 3.3.
+    """
+    def __init__(self, requires_grad=False):
+        super(Vgg19, self).__init__()
+        vgg_pretrained_features = models.vgg19(pretrained=True).features
+        self.slice1 = torch.nn.Sequential()
+        self.slice2 = torch.nn.Sequential()
+        self.slice3 = torch.nn.Sequential()
+        self.slice4 = torch.nn.Sequential()
+        self.slice5 = torch.nn.Sequential()
+        for x in range(2):
+            self.slice1.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(2, 7):
+            self.slice2.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(7, 12):
+            self.slice3.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(12, 21):
+            self.slice4.add_module(str(x), vgg_pretrained_features[x])
+        for x in range(21, 30):
+            self.slice5.add_module(str(x), vgg_pretrained_features[x])
+
+        self.mean = torch.nn.Parameter(data=torch.Tensor(np.array([0.485, 0.456, 0.406]).reshape((1, 3, 1, 1))),
+                                       requires_grad=False)
+        self.std = torch.nn.Parameter(data=torch.Tensor(np.array([0.229, 0.224, 0.225]).reshape((1, 3, 1, 1))),
+                                      requires_grad=False)
+
+        if not requires_grad:
+            for param in self.parameters():
+                param.requires_grad = False
+
+    def forward(self, X):
+        X = (X - self.mean) / self.std
+        h_relu1 = self.slice1(X)
+        h_relu2 = self.slice2(h_relu1)
+        h_relu3 = self.slice3(h_relu2)
+        h_relu4 = self.slice4(h_relu3)
+        h_relu5 = self.slice5(h_relu4)
+        out = [h_relu1, h_relu2, h_relu3, h_relu4, h_relu5]
+        return out
+
+
+class ImagePyramide(torch.nn.Module):
+    """
+    Create image pyramide for computing pyramide perceptual loss. See Sec 3.3
+    """
+    def __init__(self, scales, num_channels):
+        super(ImagePyramide, self).__init__()
+        downs = {}
+        for scale in scales:
+            downs[str(scale).replace('.', '-')] = AntiAliasInterpolation2d(num_channels, scale)
+        self.downs = nn.ModuleDict(downs)
+
+    def forward(self, x):
+        out_dict = {}
+        for scale, down_module in self.downs.items():
+            out_dict['prediction_' + str(scale).replace('-', '.')] = down_module(x)
+        return out_dict
+
+
+def detach_kp(kp):
+    return {key: value.detach() for key, value in kp.items()}
+
+
+class GeneratorFullModel(torch.nn.Module):
+    """
+    Merge all generator related updates into single model for better multi-gpu usage
+    """
+
+    def __init__(self, kp_extractor, bg_predictor, dense_motion_network, inpainting_network, train_params, *kwargs):
+        super(GeneratorFullModel, self).__init__()
+        self.kp_extractor = kp_extractor
+        self.inpainting_network = inpainting_network
+        self.dense_motion_network = dense_motion_network
+
+        self.bg_predictor = None
+        if bg_predictor:
+            self.bg_predictor = bg_predictor
+            self.bg_start = train_params['bg_start']
+
+        self.train_params = train_params
+        self.scales = train_params['scales']
+
+        self.pyramid = ImagePyramide(self.scales, inpainting_network.num_channels)
+        if torch.cuda.is_available():
+            self.pyramid = self.pyramid.cuda()
+
+        self.loss_weights = train_params['loss_weights']
+        self.dropout_epoch = train_params['dropout_epoch']
+        self.dropout_maxp = train_params['dropout_maxp']
+        self.dropout_inc_epoch = train_params['dropout_inc_epoch']
+        self.dropout_startp =train_params['dropout_startp']
+        
+        if sum(self.loss_weights['perceptual']) != 0:
+            self.vgg = Vgg19()
+            if torch.cuda.is_available():
+                self.vgg = self.vgg.cuda()
+
+
+    def forward(self, x, epoch):
+        kp_source = self.kp_extractor(x['source'])
+        kp_driving = self.kp_extractor(x['driving'])
+        bg_param = None
+        if self.bg_predictor:
+            if(epoch>=self.bg_start):
+                bg_param = self.bg_predictor(x['source'], x['driving'])
+          
+        if(epoch>=self.dropout_epoch):
+            dropout_flag = False
+            dropout_p = 0
+        else:
+            # dropout_p will linearly increase from dropout_startp to dropout_maxp 
+            dropout_flag = True
+            dropout_p = min(epoch/self.dropout_inc_epoch * self.dropout_maxp + self.dropout_startp, self.dropout_maxp)
+        
+        dense_motion = self.dense_motion_network(source_image=x['source'], kp_driving=kp_driving,
+                                                    kp_source=kp_source, bg_param = bg_param, 
+                                                    dropout_flag = dropout_flag, dropout_p = dropout_p)
+        generated = self.inpainting_network(x['source'], dense_motion)
+        generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
+
+        loss_values = {}
+
+        pyramide_real = self.pyramid(x['driving'])
+        pyramide_generated = self.pyramid(generated['prediction'])
+
+        # reconstruction loss
+        if sum(self.loss_weights['perceptual']) != 0:
+            value_total = 0
+            for scale in self.scales:
+                x_vgg = self.vgg(pyramide_generated['prediction_' + str(scale)])
+                y_vgg = self.vgg(pyramide_real['prediction_' + str(scale)])
+
+                for i, weight in enumerate(self.loss_weights['perceptual']):
+                    value = torch.abs(x_vgg[i] - y_vgg[i].detach()).mean()
+                    value_total += self.loss_weights['perceptual'][i] * value
+            loss_values['perceptual'] = value_total
+
+        # equivariance loss
+        if self.loss_weights['equivariance_value'] != 0:
+            transform_random = TPS(mode = 'random', bs = x['driving'].shape[0], **self.train_params['transform_params'])
+            transform_grid = transform_random.transform_frame(x['driving'])
+            transformed_frame = F.grid_sample(x['driving'], transform_grid, padding_mode="reflection",align_corners=True)
+            transformed_kp = self.kp_extractor(transformed_frame)
+
+            generated['transformed_frame'] = transformed_frame
+            generated['transformed_kp'] = transformed_kp
+        
+            warped = transform_random.warp_coordinates(transformed_kp['fg_kp'])
+            kp_d = kp_driving['fg_kp']
+            value = torch.abs(kp_d - warped).mean()
+            loss_values['equivariance_value'] = self.loss_weights['equivariance_value'] * value
+
+        # warp loss
+        if self.loss_weights['warp_loss'] != 0:
+            occlusion_map = generated['occlusion_map']
+            encode_map = self.inpainting_network.get_encode(x['driving'], occlusion_map)
+            decode_map = generated['warped_encoder_maps']
+            value = 0
+            for i in range(len(encode_map)):
+                value += torch.abs(encode_map[i]-decode_map[-i-1]).mean()
+
+            loss_values['warp_loss'] = self.loss_weights['warp_loss'] * value
+        
+        # bg loss
+        if self.bg_predictor and epoch >= self.bg_start and self.loss_weights['bg'] != 0:
+            bg_param_reverse = self.bg_predictor(x['driving'], x['source'])
+            value = torch.matmul(bg_param, bg_param_reverse)
+            eye = torch.eye(3).view(1, 1, 3, 3).type(value.type())
+            value = torch.abs(eye - value).mean()
+            loss_values['bg'] = self.loss_weights['bg'] * value
+
+        return loss_values, generated

Thin-Plate-Spline-Motion-Model/modules/util.py

@@ -0,0 +1,349 @@
+from torch import nn
+import torch.nn.functional as F
+import torch
+
+
+class TPS:
+    '''
+    TPS transformation, mode 'kp' for Eq(2) in the paper, mode 'random' for equivariance loss.
+    '''
+    def __init__(self, mode, bs, **kwargs):
+        self.bs = bs
+        self.mode = mode
+        if mode == 'random':
+            noise = torch.normal(mean=0, std=kwargs['sigma_affine'] * torch.ones([bs, 2, 3]))
+            self.theta = noise + torch.eye(2, 3).view(1, 2, 3)
+            self.control_points = make_coordinate_grid((kwargs['points_tps'], kwargs['points_tps']), type=noise.type())
+            self.control_points = self.control_points.unsqueeze(0)
+            self.control_params = torch.normal(mean=0, 
+                        std=kwargs['sigma_tps'] * torch.ones([bs, 1, kwargs['points_tps'] ** 2]))
+        elif mode == 'kp':
+            kp_1 = kwargs["kp_1"]
+            kp_2 = kwargs["kp_2"]
+            device = kp_1.device
+            kp_type = kp_1.type()
+            self.gs = kp_1.shape[1]
+            n = kp_1.shape[2]
+            K = torch.norm(kp_1[:,:,:, None]-kp_1[:,:, None, :], dim=4, p=2)
+            K = K**2
+            K = K * torch.log(K+1e-9)
+            
+            one1 = torch.ones(self.bs, kp_1.shape[1], kp_1.shape[2], 1).to(device).type(kp_type)
+            kp_1p = torch.cat([kp_1,one1], 3)
+            
+            zero = torch.zeros(self.bs, kp_1.shape[1], 3, 3).to(device).type(kp_type)
+            P = torch.cat([kp_1p, zero],2)
+            L = torch.cat([K,kp_1p.permute(0,1,3,2)],2)
+            L = torch.cat([L,P],3)
+        
+            zero = torch.zeros(self.bs, kp_1.shape[1], 3, 2).to(device).type(kp_type)
+            Y = torch.cat([kp_2, zero], 2)
+            one = torch.eye(L.shape[2]).expand(L.shape).to(device).type(kp_type)*0.01
+            L = L + one
+
+            param = torch.matmul(torch.inverse(L),Y)
+            self.theta = param[:,:,n:,:].permute(0,1,3,2)
+
+            self.control_points = kp_1
+            self.control_params = param[:,:,:n,:]
+        else:
+            raise Exception("Error TPS mode")
+
+    def transform_frame(self, frame):
+        grid = make_coordinate_grid(frame.shape[2:], type=frame.type()).unsqueeze(0).to(frame.device)
+        grid = grid.view(1, frame.shape[2] * frame.shape[3], 2)
+        shape = [self.bs, frame.shape[2], frame.shape[3], 2]
+        if self.mode == 'kp':
+            shape.insert(1, self.gs)
+        grid = self.warp_coordinates(grid).view(*shape)
+        return grid
+
+    def warp_coordinates(self, coordinates):
+        theta = self.theta.type(coordinates.type()).to(coordinates.device)
+        control_points = self.control_points.type(coordinates.type()).to(coordinates.device)
+        control_params = self.control_params.type(coordinates.type()).to(coordinates.device)
+
+        if self.mode == 'kp':
+            transformed = torch.matmul(theta[:, :, :, :2], coordinates.permute(0, 2, 1)) + theta[:, :, :, 2:]
+
+            distances = coordinates.view(coordinates.shape[0], 1, 1, -1, 2) - control_points.view(self.bs, control_points.shape[1], -1, 1, 2)
+
+            distances = distances ** 2
+            result = distances.sum(-1)
+            result = result * torch.log(result + 1e-9)
+            result = torch.matmul(result.permute(0, 1, 3, 2), control_params)
+            transformed = transformed.permute(0, 1, 3, 2) + result
+
+        elif self.mode == 'random':
+            theta = theta.unsqueeze(1)
+            transformed = torch.matmul(theta[:, :, :, :2], coordinates.unsqueeze(-1)) + theta[:, :, :, 2:]
+            transformed = transformed.squeeze(-1)
+            ances = coordinates.view(coordinates.shape[0], -1, 1, 2) - control_points.view(1, 1, -1, 2)
+            distances = ances ** 2
+
+            result = distances.sum(-1)
+            result = result * torch.log(result + 1e-9)
+            result = result * control_params
+            result = result.sum(dim=2).view(self.bs, coordinates.shape[1], 1)
+            transformed = transformed + result
+        else:
+            raise Exception("Error TPS mode")
+
+        return transformed
+        
+
+def kp2gaussian(kp, spatial_size, kp_variance):
+    """
+    Transform a keypoint into gaussian like representation
+    """
+
+    coordinate_grid = make_coordinate_grid(spatial_size, kp.type()).to(kp.device)
+    number_of_leading_dimensions = len(kp.shape) - 1
+    shape = (1,) * number_of_leading_dimensions + coordinate_grid.shape
+    coordinate_grid = coordinate_grid.view(*shape)
+    repeats = kp.shape[:number_of_leading_dimensions] + (1, 1, 1)
+    coordinate_grid = coordinate_grid.repeat(*repeats)
+
+    # Preprocess kp shape
+    shape = kp.shape[:number_of_leading_dimensions] + (1, 1, 2)
+    kp = kp.view(*shape)
+
+    mean_sub = (coordinate_grid - kp)
+
+    out = torch.exp(-0.5 * (mean_sub ** 2).sum(-1) / kp_variance)
+
+    return out
+
+
+def make_coordinate_grid(spatial_size, type):
+    """
+    Create a meshgrid [-1,1] x [-1,1] of given spatial_size.
+    """
+    h, w = spatial_size
+    x = torch.arange(w).type(type)
+    y = torch.arange(h).type(type)
+
+    x = (2 * (x / (w - 1)) - 1)
+    y = (2 * (y / (h - 1)) - 1)
+
+    yy = y.view(-1, 1).repeat(1, w)
+    xx = x.view(1, -1).repeat(h, 1)
+
+    meshed = torch.cat([xx.unsqueeze_(2), yy.unsqueeze_(2)], 2)
+
+    return meshed
+
+
+class ResBlock2d(nn.Module):
+    """
+    Res block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, kernel_size, padding):
+        super(ResBlock2d, self).__init__()
+        self.conv1 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.conv2 = nn.Conv2d(in_channels=in_features, out_channels=in_features, kernel_size=kernel_size,
+                               padding=padding)
+        self.norm1 = nn.InstanceNorm2d(in_features, affine=True)
+        self.norm2 = nn.InstanceNorm2d(in_features, affine=True)
+
+    def forward(self, x):
+        out = self.norm1(x)
+        out = F.relu(out)
+        out = self.conv1(out)
+        out = self.norm2(out)
+        out = F.relu(out)
+        out = self.conv2(out)
+        out += x
+        return out
+
+
+class UpBlock2d(nn.Module):
+    """
+    Upsampling block for use in decoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(UpBlock2d, self).__init__()
+
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = nn.InstanceNorm2d(out_features, affine=True)
+
+    def forward(self, x):
+        out = F.interpolate(x, scale_factor=2)
+        out = self.conv(out)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class DownBlock2d(nn.Module):
+    """
+    Downsampling block for use in encoder.
+    """
+
+    def __init__(self, in_features, out_features, kernel_size=3, padding=1, groups=1):
+        super(DownBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features, kernel_size=kernel_size,
+                              padding=padding, groups=groups)
+        self.norm = nn.InstanceNorm2d(out_features, affine=True)
+        self.pool = nn.AvgPool2d(kernel_size=(2, 2))
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        out = self.pool(out)
+        return out
+
+
+class SameBlock2d(nn.Module):
+    """
+    Simple block, preserve spatial resolution.
+    """
+
+    def __init__(self, in_features, out_features, groups=1, kernel_size=3, padding=1):
+        super(SameBlock2d, self).__init__()
+        self.conv = nn.Conv2d(in_channels=in_features, out_channels=out_features,
+                              kernel_size=kernel_size, padding=padding, groups=groups)
+        self.norm = nn.InstanceNorm2d(out_features, affine=True)
+
+    def forward(self, x):
+        out = self.conv(x)
+        out = self.norm(out)
+        out = F.relu(out)
+        return out
+
+
+class Encoder(nn.Module):
+    """
+    Hourglass Encoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Encoder, self).__init__()
+
+        down_blocks = []
+        for i in range(num_blocks):
+            down_blocks.append(DownBlock2d(in_features if i == 0 else min(max_features, block_expansion * (2 ** i)),
+                                           min(max_features, block_expansion * (2 ** (i + 1))),
+                                           kernel_size=3, padding=1))
+        self.down_blocks = nn.ModuleList(down_blocks)
+
+    def forward(self, x):
+        outs = [x]
+        #print('encoder:' ,outs[-1].shape)
+        for down_block in self.down_blocks:
+            outs.append(down_block(outs[-1]))
+            #print('encoder:' ,outs[-1].shape)
+        return outs
+
+
+class Decoder(nn.Module):
+    """
+    Hourglass Decoder
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Decoder, self).__init__()
+
+        up_blocks = []
+        self.out_channels = []
+        for i in range(num_blocks)[::-1]:
+            in_filters = (1 if i == num_blocks - 1 else 2) * min(max_features, block_expansion * (2 ** (i + 1)))
+            self.out_channels.append(in_filters)
+            out_filters = min(max_features, block_expansion * (2 ** i))
+            up_blocks.append(UpBlock2d(in_filters, out_filters, kernel_size=3, padding=1))
+
+        self.up_blocks = nn.ModuleList(up_blocks)
+        self.out_channels.append(block_expansion + in_features)
+        # self.out_filters = block_expansion + in_features
+
+    def forward(self, x, mode = 0):
+        out = x.pop()
+        outs = []
+        for up_block in self.up_blocks:
+            out = up_block(out)
+            skip = x.pop()
+            out = torch.cat([out, skip], dim=1)
+            outs.append(out)
+        if(mode == 0):
+            return out
+        else:
+            return outs
+
+
+class Hourglass(nn.Module):
+    """
+    Hourglass architecture.
+    """
+
+    def __init__(self, block_expansion, in_features, num_blocks=3, max_features=256):
+        super(Hourglass, self).__init__()
+        self.encoder = Encoder(block_expansion, in_features, num_blocks, max_features)
+        self.decoder = Decoder(block_expansion, in_features, num_blocks, max_features)
+        self.out_channels = self.decoder.out_channels
+        # self.out_filters = self.decoder.out_filters
+
+    def forward(self, x, mode = 0):
+        return self.decoder(self.encoder(x), mode)
+
+
+class AntiAliasInterpolation2d(nn.Module):
+    """
+    Band-limited downsampling, for better preservation of the input signal.
+    """
+    def __init__(self, channels, scale):
+        super(AntiAliasInterpolation2d, self).__init__()
+        sigma = (1 / scale - 1) / 2
+        kernel_size = 2 * round(sigma * 4) + 1
+        self.ka = kernel_size // 2
+        self.kb = self.ka - 1 if kernel_size % 2 == 0 else self.ka
+
+        kernel_size = [kernel_size, kernel_size]
+        sigma = [sigma, sigma]
+        # The gaussian kernel is the product of the
+        # gaussian function of each dimension.
+        kernel = 1
+        meshgrids = torch.meshgrid(
+            [
+                torch.arange(size, dtype=torch.float32)
+                for size in kernel_size
+                ]
+        )
+        for size, std, mgrid in zip(kernel_size, sigma, meshgrids):
+            mean = (size - 1) / 2
+            kernel *= torch.exp(-(mgrid - mean) ** 2 / (2 * std ** 2))
+
+        # Make sure sum of values in gaussian kernel equals 1.
+        kernel = kernel / torch.sum(kernel)
+        # Reshape to depthwise convolutional weight
+        kernel = kernel.view(1, 1, *kernel.size())
+        kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1))
+
+        self.register_buffer('weight', kernel)
+        self.groups = channels
+        self.scale = scale
+
+    def forward(self, input):
+        if self.scale == 1.0:
+            return input
+
+        out = F.pad(input, (self.ka, self.kb, self.ka, self.kb))
+        out = F.conv2d(out, weight=self.weight, groups=self.groups)
+        out = F.interpolate(out, scale_factor=(self.scale, self.scale))
+
+        return out
+
+
+def to_homogeneous(coordinates):
+    ones_shape = list(coordinates.shape)
+    ones_shape[-1] = 1
+    ones = torch.ones(ones_shape).type(coordinates.type())
+
+    return torch.cat([coordinates, ones], dim=-1)
+
+def from_homogeneous(coordinates):
+    return coordinates[..., :2] / coordinates[..., 2:3]

Thin-Plate-Spline-Motion-Model/predict.py

@@ -0,0 +1,125 @@
+import os
+import sys
+sys.path.insert(0, "stylegan-encoder")
+import tempfile
+import warnings
+import imageio
+import numpy as np
+import matplotlib.pyplot as plt
+import matplotlib.animation as animation
+from skimage.transform import resize
+from skimage import img_as_ubyte
+import torch
+import torchvision.transforms as transforms
+import dlib
+from cog import BasePredictor, Path, Input
+
+from demo import load_checkpoints
+from demo import make_animation
+from ffhq_dataset.face_alignment import image_align
+from ffhq_dataset.landmarks_detector import LandmarksDetector
+
+
+warnings.filterwarnings("ignore")
+
+
+PREDICTOR = dlib.shape_predictor("shape_predictor_68_face_landmarks.dat")
+LANDMARKS_DETECTOR = LandmarksDetector("shape_predictor_68_face_landmarks.dat")
+
+
+class Predictor(BasePredictor):
+    def setup(self):
+
+        self.device = torch.device("cuda:0")
+        datasets = ["vox", "taichi", "ted", "mgif"]
+        (
+            self.inpainting,
+            self.kp_detector,
+            self.dense_motion_network,
+            self.avd_network,
+        ) = ({}, {}, {}, {})
+        for d in datasets:
+            (
+                self.inpainting[d],
+                self.kp_detector[d],
+                self.dense_motion_network[d],
+                self.avd_network[d],
+            ) = load_checkpoints(
+                config_path=f"config/{d}-384.yaml"
+                if d == "ted"
+                else f"config/{d}-256.yaml",
+                checkpoint_path=f"checkpoints/{d}.pth.tar",
+                device=self.device,
+            )
+
+    def predict(
+        self,
+        source_image: Path = Input(
+            description="Input source image.",
+        ),
+        driving_video: Path = Input(
+            description="Choose a micromotion.",
+        ),
+        dataset_name: str = Input(
+            choices=["vox", "taichi", "ted", "mgif"],
+            default="vox",
+            description="Choose a dataset.",
+        ),
+    ) -> Path:
+
+        predict_mode = "relative"  # ['standard', 'relative', 'avd']
+        # find_best_frame = False
+
+        pixel = 384 if dataset_name == "ted" else 256
+
+        if dataset_name == "vox":
+            # first run face alignment
+            align_image(str(source_image), 'aligned.png')
+            source_image = imageio.imread('aligned.png')
+        else:
+            source_image = imageio.imread(str(source_image))
+        reader = imageio.get_reader(str(driving_video))
+        fps = reader.get_meta_data()["fps"]
+        source_image = resize(source_image, (pixel, pixel))[..., :3]
+
+        driving_video = []
+        try:
+            for im in reader:
+                driving_video.append(im)
+        except RuntimeError:
+            pass
+        reader.close()
+
+        driving_video = [
+            resize(frame, (pixel, pixel))[..., :3] for frame in driving_video
+        ]
+
+        inpainting, kp_detector, dense_motion_network, avd_network = (
+            self.inpainting[dataset_name],
+            self.kp_detector[dataset_name],
+            self.dense_motion_network[dataset_name],
+            self.avd_network[dataset_name],
+        )
+
+        predictions = make_animation(
+            source_image,
+            driving_video,
+            inpainting,
+            kp_detector,
+            dense_motion_network,
+            avd_network,
+            device="cuda:0",
+            mode=predict_mode,
+        )
+
+        # save resulting video
+        out_path = Path(tempfile.mkdtemp()) / "output.mp4"
+        imageio.mimsave(
+            str(out_path), [img_as_ubyte(frame) for frame in predictions], fps=fps
+        )
+        return out_path
+
+
+def align_image(raw_img_path, aligned_face_path):
+    for i, face_landmarks in enumerate(LANDMARKS_DETECTOR.get_landmarks(raw_img_path), start=1):
+        image_align(raw_img_path, aligned_face_path, face_landmarks)

Thin-Plate-Spline-Motion-Model/reconstruction.py

@@ -0,0 +1,69 @@
+import os
+from tqdm import tqdm
+import torch
+from torch.utils.data import DataLoader
+from logger import Logger, Visualizer
+import numpy as np
+import imageio
+
+
+def reconstruction(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
+    png_dir = os.path.join(log_dir, 'reconstruction/png')
+    log_dir = os.path.join(log_dir, 'reconstruction')
+
+    if checkpoint is not None:
+        Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
+                         bg_predictor=bg_predictor, dense_motion_network=dense_motion_network)
+    else:
+        raise AttributeError("Checkpoint should be specified for mode='reconstruction'.")
+    dataloader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=1)
+
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+
+    if not os.path.exists(png_dir):
+        os.makedirs(png_dir)
+    
+    loss_list = []
+
+    inpainting_network.eval()
+    kp_detector.eval()
+    dense_motion_network.eval()
+    if bg_predictor:
+        bg_predictor.eval()
+
+    for it, x in tqdm(enumerate(dataloader)):
+        with torch.no_grad():
+            predictions = []
+            visualizations = []
+            if torch.cuda.is_available():
+                x['video'] = x['video'].cuda()
+            kp_source = kp_detector(x['video'][:, :, 0])
+            for frame_idx in range(x['video'].shape[2]):
+                source = x['video'][:, :, 0]
+                driving = x['video'][:, :, frame_idx]
+                kp_driving = kp_detector(driving)
+                bg_params = None
+                if bg_predictor:
+                    bg_params = bg_predictor(source, driving)
+                
+                dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
+                                                    kp_source=kp_source, bg_param = bg_params, 
+                                                    dropout_flag = False)
+                out = inpainting_network(source, dense_motion)
+                out['kp_source'] = kp_source
+                out['kp_driving'] = kp_driving
+
+                predictions.append(np.transpose(out['prediction'].data.cpu().numpy(), [0, 2, 3, 1])[0])
+
+                visualization = Visualizer(**config['visualizer_params']).visualize(source=source,
+                                                                                   driving=driving, out=out)
+                visualizations.append(visualization)
+                loss = torch.abs(out['prediction'] - driving).mean().cpu().numpy()
+                
+                loss_list.append(loss)
+            # print(np.mean(loss_list))
+            predictions = np.concatenate(predictions, axis=1)
+            imageio.imsave(os.path.join(png_dir, x['name'][0] + '.png'), (255 * predictions).astype(np.uint8))
+
+    print("Reconstruction loss: %s" % np.mean(loss_list))

Thin-Plate-Spline-Motion-Model/requirements.txt

@@ -0,0 +1,25 @@
+cffi==1.14.6
+cycler==0.10.0
+decorator==5.1.0
+face-alignment==1.3.5
+imageio==2.9.0
+imageio-ffmpeg==0.4.5
+kiwisolver==1.3.2
+matplotlib==3.4.3
+networkx==2.6.3
+numpy==1.20.3
+pandas==1.3.3
+Pillow==8.3.2
+pycparser==2.20
+pyparsing==2.4.7
+python-dateutil==2.8.2
+pytz==2021.1
+PyWavelets==1.1.1
+PyYAML==5.4.1
+scikit-image==0.18.3
+scikit-learn==1.0
+scipy==1.7.1
+six==1.16.0
+torch==1.10.0+cu113
+torchvision==0.11.0+cu113
+tqdm==4.62.3

Thin-Plate-Spline-Motion-Model/run.py

@@ -0,0 +1,89 @@
+import matplotlib
+matplotlib.use('Agg')
+
+import os, sys
+import yaml
+from argparse import ArgumentParser
+from time import gmtime, strftime
+from shutil import copy
+from frames_dataset import FramesDataset
+
+from modules.inpainting_network import InpaintingNetwork
+from modules.keypoint_detector import KPDetector
+from modules.bg_motion_predictor import BGMotionPredictor
+from modules.dense_motion import DenseMotionNetwork
+from modules.avd_network import AVDNetwork
+import torch
+from train import train
+from train_avd import train_avd
+from reconstruction import reconstruction
+import os 
+
+
+if __name__ == "__main__":
+    
+    if sys.version_info[0] < 3:
+        raise Exception("You must use Python 3 or higher. Recommended version is Python 3.9")
+
+    parser = ArgumentParser()
+    parser.add_argument("--config", default="config/vox-256.yaml", help="path to config")
+    parser.add_argument("--mode", default="train", choices=["train", "reconstruction", "train_avd"])
+    parser.add_argument("--log_dir", default='log', help="path to log into")
+    parser.add_argument("--checkpoint", default=None, help="path to checkpoint to restore")
+    parser.add_argument("--device_ids", default="0,1", type=lambda x: list(map(int, x.split(','))),
+                        help="Names of the devices comma separated.")
+
+    opt = parser.parse_args()
+    with open(opt.config) as f:
+        config = yaml.load(f)
+
+    if opt.checkpoint is not None:
+        log_dir = os.path.join(*os.path.split(opt.checkpoint)[:-1])
+    else:
+        log_dir = os.path.join(opt.log_dir, os.path.basename(opt.config).split('.')[0])
+        log_dir += ' ' + strftime("%d_%m_%y_%H.%M.%S", gmtime())
+
+    inpainting = InpaintingNetwork(**config['model_params']['generator_params'],
+                                        **config['model_params']['common_params'])
+
+    if torch.cuda.is_available():
+        cuda_device = torch.device('cuda:'+str(opt.device_ids[0]))
+        inpainting.to(cuda_device)
+
+    kp_detector = KPDetector(**config['model_params']['common_params'])
+    dense_motion_network = DenseMotionNetwork(**config['model_params']['common_params'],
+                                              **config['model_params']['dense_motion_params'])
+                                                           
+    if torch.cuda.is_available():
+        kp_detector.to(opt.device_ids[0])
+        dense_motion_network.to(opt.device_ids[0])
+
+    bg_predictor = None
+    if (config['model_params']['common_params']['bg']):
+        bg_predictor = BGMotionPredictor()
+        if torch.cuda.is_available():
+            bg_predictor.to(opt.device_ids[0])
+
+    avd_network = None
+    if opt.mode == "train_avd":
+        avd_network = AVDNetwork(num_tps=config['model_params']['common_params']['num_tps'],
+                             **config['model_params']['avd_network_params'])
+        if torch.cuda.is_available():
+            avd_network.to(opt.device_ids[0])
+
+    dataset = FramesDataset(is_train=(opt.mode.startswith('train')), **config['dataset_params'])
+
+    if not os.path.exists(log_dir):
+        os.makedirs(log_dir)
+    if not os.path.exists(os.path.join(log_dir, os.path.basename(opt.config))):
+        copy(opt.config, log_dir)
+
+    if opt.mode == 'train':
+        print("Training...")
+        train(config, inpainting, kp_detector, bg_predictor, dense_motion_network, opt.checkpoint, log_dir, dataset)
+    elif opt.mode == 'train_avd':
+        print("Training Animation via Disentaglement...")
+        train_avd(config, inpainting, kp_detector, bg_predictor, dense_motion_network, avd_network, opt.checkpoint, log_dir, dataset)
+    elif opt.mode == 'reconstruction':
+        print("Reconstruction...")
+        reconstruction(config, inpainting, kp_detector, bg_predictor, dense_motion_network, opt.checkpoint, log_dir, dataset)

Thin-Plate-Spline-Motion-Model/train.py

@@ -0,0 +1,94 @@
+from tqdm import trange
+import torch
+from torch.utils.data import DataLoader
+from logger import Logger
+from modules.model import GeneratorFullModel
+from torch.optim.lr_scheduler import MultiStepLR
+from torch.nn.utils import clip_grad_norm_
+from frames_dataset import DatasetRepeater
+import math
+
+def train(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, checkpoint, log_dir, dataset):
+    train_params = config['train_params']
+    optimizer = torch.optim.Adam(
+        [{'params': list(inpainting_network.parameters()) +
+                    list(dense_motion_network.parameters()) +
+                    list(kp_detector.parameters()), 'initial_lr': train_params['lr_generator']}],lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4)
+    
+    optimizer_bg_predictor = None
+    if bg_predictor:
+        optimizer_bg_predictor = torch.optim.Adam(
+            [{'params':bg_predictor.parameters(),'initial_lr': train_params['lr_generator']}], 
+            lr=train_params['lr_generator'], betas=(0.5, 0.999), weight_decay = 1e-4)
+
+    if checkpoint is not None:
+        start_epoch = Logger.load_cpk(
+            checkpoint, inpainting_network = inpainting_network, dense_motion_network = dense_motion_network,       
+            kp_detector = kp_detector, bg_predictor = bg_predictor,
+            optimizer = optimizer, optimizer_bg_predictor = optimizer_bg_predictor)
+        print('load success:', start_epoch)
+        start_epoch += 1
+    else:
+        start_epoch = 0
+
+    scheduler_optimizer = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1,
+                                      last_epoch=start_epoch - 1)
+    if bg_predictor:
+        scheduler_bg_predictor = MultiStepLR(optimizer_bg_predictor, train_params['epoch_milestones'],
+                                              gamma=0.1, last_epoch=start_epoch - 1)
+
+    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
+        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
+    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True, 
+                            num_workers=train_params['dataloader_workers'], drop_last=True)
+
+    generator_full = GeneratorFullModel(kp_detector, bg_predictor, dense_motion_network, inpainting_network, train_params)
+
+    if torch.cuda.is_available():
+        generator_full = torch.nn.DataParallel(generator_full).cuda()  
+        
+    bg_start = train_params['bg_start']
+    
+    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], 
+                checkpoint_freq=train_params['checkpoint_freq']) as logger:
+        for epoch in trange(start_epoch, train_params['num_epochs']):
+            for x in dataloader:
+                if(torch.cuda.is_available()):
+                    x['driving'] = x['driving'].cuda()
+                    x['source'] = x['source'].cuda()
+
+                losses_generator, generated = generator_full(x, epoch)
+                loss_values = [val.mean() for val in losses_generator.values()]
+                loss = sum(loss_values)
+                loss.backward()
+
+                clip_grad_norm_(kp_detector.parameters(), max_norm=10, norm_type = math.inf)
+                clip_grad_norm_(dense_motion_network.parameters(), max_norm=10, norm_type = math.inf)
+                if bg_predictor and epoch>=bg_start:
+                    clip_grad_norm_(bg_predictor.parameters(), max_norm=10, norm_type = math.inf)
+                
+                optimizer.step()
+                optimizer.zero_grad()
+                if bg_predictor and epoch>=bg_start:
+                    optimizer_bg_predictor.step()
+                    optimizer_bg_predictor.zero_grad()
+                
+                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in losses_generator.items()}
+                logger.log_iter(losses=losses)
+
+            scheduler_optimizer.step()
+            if bg_predictor:
+                scheduler_bg_predictor.step()
+            
+            model_save = {
+                'inpainting_network': inpainting_network,
+                'dense_motion_network': dense_motion_network,
+                'kp_detector': kp_detector,
+                'optimizer': optimizer,
+            }
+            if bg_predictor and epoch>=bg_start:
+                model_save['bg_predictor'] = bg_predictor
+                model_save['optimizer_bg_predictor'] = optimizer_bg_predictor
+            
+            logger.log_epoch(epoch, model_save, inp=x, out=generated)
+

Thin-Plate-Spline-Motion-Model/train_avd.py

@@ -0,0 +1,91 @@
+from tqdm import trange
+import torch
+from torch.utils.data import DataLoader
+from logger import Logger
+from torch.optim.lr_scheduler import MultiStepLR
+from frames_dataset import DatasetRepeater
+
+
+def random_scale(kp_params, scale):
+    theta = torch.rand(kp_params['fg_kp'].shape[0], 2) * (2 * scale) + (1 - scale)
+    theta = torch.diag_embed(theta).unsqueeze(1).type(kp_params['fg_kp'].type())
+    new_kp_params = {'fg_kp': torch.matmul(theta, kp_params['fg_kp'].unsqueeze(-1)).squeeze(-1)}
+    return new_kp_params
+
+
+def train_avd(config, inpainting_network, kp_detector, bg_predictor, dense_motion_network, 
+              avd_network, checkpoint, log_dir, dataset):
+    train_params = config['train_avd_params']
+
+    optimizer = torch.optim.Adam(avd_network.parameters(), lr=train_params['lr'], betas=(0.5, 0.999))
+
+    if checkpoint is not None:
+        Logger.load_cpk(checkpoint, inpainting_network=inpainting_network, kp_detector=kp_detector,
+                                      bg_predictor=bg_predictor, avd_network=avd_network,
+                                      dense_motion_network= dense_motion_network,optimizer_avd=optimizer)
+        start_epoch = 0
+    else:
+        raise AttributeError("Checkpoint should be specified for mode='train_avd'.")
+
+    scheduler = MultiStepLR(optimizer, train_params['epoch_milestones'], gamma=0.1)
+
+    if 'num_repeats' in train_params or train_params['num_repeats'] != 1:
+        dataset = DatasetRepeater(dataset, train_params['num_repeats'])
+
+    dataloader = DataLoader(dataset, batch_size=train_params['batch_size'], shuffle=True,
+                            num_workers=train_params['dataloader_workers'], drop_last=True)
+
+    with Logger(log_dir=log_dir, visualizer_params=config['visualizer_params'], 
+                checkpoint_freq=train_params['checkpoint_freq']) as logger:
+        for epoch in trange(start_epoch, train_params['num_epochs']):
+            avd_network.train()
+            for x in dataloader:
+                with torch.no_grad():
+                    kp_source = kp_detector(x['source'].cuda())
+                    kp_driving_gt = kp_detector(x['driving'].cuda())
+                    kp_driving_random = random_scale(kp_driving_gt, scale=train_params['random_scale'])
+                rec = avd_network(kp_source, kp_driving_random)
+
+                reconstruction_kp = train_params['lambda_shift'] * \
+                                       torch.abs(kp_driving_gt['fg_kp'] - rec['fg_kp']).mean()
+                
+                loss_dict = {'rec_kp': reconstruction_kp}
+                loss = reconstruction_kp
+                
+                loss.backward()
+                optimizer.step()
+                optimizer.zero_grad()
+
+                losses = {key: value.mean().detach().data.cpu().numpy() for key, value in loss_dict.items()}
+                logger.log_iter(losses=losses)
+
+            # Visualization
+            avd_network.eval()
+            with torch.no_grad():
+                source = x['source'][:6].cuda()
+                driving = torch.cat([x['driving'][[0, 1]].cuda(), source[[2, 3, 2, 1]]], dim=0)
+                kp_source = kp_detector(source)
+                kp_driving = kp_detector(driving)
+
+                out = avd_network(kp_source, kp_driving)
+                kp_driving = out
+                dense_motion = dense_motion_network(source_image=source, kp_driving=kp_driving,
+                                            kp_source=kp_source)
+                generated = inpainting_network(source, dense_motion)
+
+                generated.update({'kp_source': kp_source, 'kp_driving': kp_driving})
+
+            scheduler.step(epoch)
+            model_save = {
+                'inpainting_network': inpainting_network,
+                'dense_motion_network': dense_motion_network,
+                'kp_detector': kp_detector,
+                'avd_network': avd_network,
+                'optimizer_avd': optimizer
+            }
+            if bg_predictor :
+                model_save['bg_predictor'] = bg_predictor
+
+            logger.log_epoch(epoch, model_save,
+                             inp={'source': source, 'driving': driving},
+                             out=generated)

app.py

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+				<span class="dark:text-gray-300">app.py</span></div></div>
+
+	
+	</header>
+			<div class="SVELTE_HYDRATER contents" data-props="{&quot;commitLast&quot;:{&quot;date&quot;:&quot;2023-10-25T19:06:23.000Z&quot;,&quot;subject&quot;:&quot;Fix dependency issues and add some updates (#10)&quot;,&quot;authors&quot;:[{&quot;_id&quot;:&quot;605b1a536ce6cabbb3474b5a&quot;,&quot;avatar&quot;:&quot;https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1655583590216-605b1a536ce6cabbb3474b5a.jpeg?w=200&amp;h=200&amp;f=face&quot;,&quot;isHf&quot;:false,&quot;user&quot;:&quot;animesh007&quot;}],&quot;commit&quot;:{&quot;id&quot;:&quot;626d7ffc7b07ba96290bd80cb09b73993f85bf40&quot;,&quot;parentIds&quot;:[&quot;6614b1ace6e25e6d94942514c1e9b6e9d3942f46&quot;]},&quot;title&quot;:&quot;Fix dependency issues and add some updates (<a href=\&quot;/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/discussions/10\&quot;>#10</a>)&quot;},&quot;repo&quot;:{&quot;name&quot;:&quot;CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model&quot;,&quot;type&quot;:&quot;space&quot;}}" data-target="LastCommit"><div class="from-gray-100-to-white flex items-baseline rounded-t-lg border border-b-0 bg-gradient-to-t px-3 py-2 dark:border-gray-800"><img class="mr-2.5 mt-0.5 h-4 w-4 self-center rounded-full" alt="animesh007's picture" src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1655583590216-605b1a536ce6cabbb3474b5a.jpeg?w=200&amp;h=200&amp;f=face">
+			<div class="mr-5 flex flex-none items-center truncate"><a class="hover:underline" href="/animesh007">animesh007
+					</a>
+				
+			</div>
+		<div class="mr-4 truncate font-mono text-sm text-gray-500 hover:prose-a:underline"><!-- HTML_TAG_START -->Fix dependency issues and add some updates (<a href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/discussions/10">#10</a>)<!-- HTML_TAG_END --></div>
+		<a class="rounded border bg-gray-50 px-1.5 text-sm hover:underline dark:border-gray-800 dark:bg-gray-900" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/commit/626d7ffc7b07ba96290bd80cb09b73993f85bf40">626d7ff</a>
+		
+		<time class="ml-auto hidden flex-none truncate pl-2 text-gray-500 dark:text-gray-400 lg:block" datetime="2023-10-25T19:06:23" title="Wed, 25 Oct 2023 19:06:23 GMT">16 days ago</time></div></div>
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+				<a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/raw/main/app.py"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z" fill="currentColor"></path><path d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z" fill="currentColor"></path><path d="M12.419 25.484L17.639 6l1.932.518L14.35 26z" fill="currentColor"></path></svg>
+							raw
+						</a><a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/commits/main/app.py"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M16 4C9.383 4 4 9.383 4 16s5.383 12 12 12s12-5.383 12-12S22.617 4 16 4zm0 2c5.535 0 10 4.465 10 10s-4.465 10-10 10S6 21.535 6 16S10.465 6 16 6zm-1 2v9h7v-2h-5V8z" fill="currentColor"></path></svg>
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+							blame
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+							contribute
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+							delete
+						</a>
+				<div class="mr-4 flex items-center text-gray-400"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
+
+							No virus
+						</div>
+				
+				<div class="dark:text-gray-300 sm:ml-auto">4.54 kB</div></div>
+
+			<div class="relative min-h-[100px] rounded-b-lg border border-t-0 leading-tight dark:border-gray-800 dark:bg-gray-925">
+				<div class="py-3"><div class="SVELTE_HYDRATER contents" data-props="{&quot;lines&quot;:[&quot;<span class=\&quot;hljs-keyword\&quot;>import</span> os&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>import</span> pathlib&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>import</span> gradio <span class=\&quot;hljs-keyword\&quot;>as</span> gr&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>import</span> torch&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>from</span> PIL <span class=\&quot;hljs-keyword\&quot;>import</span> Image&quot;,&quot;&quot;,&quot;repo_dir = pathlib.Path(<span class=\&quot;hljs-string\&quot;>&amp;quot;Thin-Plate-Spline-Motion-Model&amp;quot;</span>).absolute()&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>if</span> <span class=\&quot;hljs-keyword\&quot;>not</span> repo_dir.exists():&quot;,&quot;    os.system(<span class=\&quot;hljs-string\&quot;>&amp;quot;git clone https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model&amp;quot;</span>)&quot;,&quot;os.chdir(repo_dir.name)&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>if</span> <span class=\&quot;hljs-keyword\&quot;>not</span> (repo_dir / <span class=\&quot;hljs-string\&quot;>&amp;quot;checkpoints&amp;quot;</span>).exists():&quot;,&quot;    os.system(<span class=\&quot;hljs-string\&quot;>&amp;quot;mkdir checkpoints&amp;quot;</span>)&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>if</span> <span class=\&quot;hljs-keyword\&quot;>not</span> (repo_dir / <span class=\&quot;hljs-string\&quot;>&amp;quot;checkpoints/vox.pth.tar&amp;quot;</span>).exists():&quot;,&quot;    os.system(<span class=\&quot;hljs-string\&quot;>&amp;quot;gdown 1-CKOjv_y_TzNe-dwQsjjeVxJUuyBAb5X -O checkpoints/vox.pth.tar&amp;quot;</span>)&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;title = <span class=\&quot;hljs-string\&quot;>&amp;quot;# Thin-Plate Spline Motion Model for Image Animation&amp;quot;</span>&quot;,&quot;DESCRIPTION = <span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;### Gradio demo for &amp;lt;b&amp;gt;Thin-Plate Spline Motion Model for Image Animation&amp;lt;/b&amp;gt;, CVPR 2022. &amp;lt;a href=&amp;#x27;https://arxiv.org/abs/2203.14367&amp;#x27;&amp;gt;[Paper]&amp;lt;/a&amp;gt;&amp;lt;a href=&amp;#x27;https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model&amp;#x27;&amp;gt;[Github Code]&amp;lt;/a&amp;gt;</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;></span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>&amp;lt;img id=&amp;quot;overview&amp;quot; alt=&amp;quot;overview&amp;quot; src=&amp;quot;https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model/raw/main/assets/vox.gif&amp;quot; /&amp;gt;</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;</span>&quot;,&quot;FOOTER = <span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;lt;img id=&amp;quot;visitor-badge&amp;quot; alt=&amp;quot;visitor badge&amp;quot; src=&amp;quot;https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.Image-Animation-using-Thin-Plate-Spline-Motion-Model&amp;quot; /&amp;gt;&amp;#x27;</span>&quot;,&quot;&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>def</span> <span class=\&quot;hljs-title function_\&quot;>get_style_image_path</span>(<span class=\&quot;hljs-params\&quot;>style_name: <span class=\&quot;hljs-built_in\&quot;>str</span></span>) -&amp;gt; <span class=\&quot;hljs-built_in\&quot;>str</span>:&quot;,&quot;    base_path = <span class=\&quot;hljs-string\&quot;>&amp;#x27;assets&amp;#x27;</span>&quot;,&quot;    filenames = {&quot;,&quot;        <span class=\&quot;hljs-string\&quot;>&amp;#x27;source&amp;#x27;</span>: <span class=\&quot;hljs-string\&quot;>&amp;#x27;source.png&amp;#x27;</span>,&quot;,&quot;        <span class=\&quot;hljs-string\&quot;>&amp;#x27;driving&amp;#x27;</span>: <span class=\&quot;hljs-string\&quot;>&amp;#x27;driving.mp4&amp;#x27;</span>,&quot;,&quot;    }&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>return</span> <span class=\&quot;hljs-string\&quot;>f&amp;#x27;<span class=\&quot;hljs-subst\&quot;>{base_path}</span>/<span class=\&quot;hljs-subst\&quot;>{filenames[style_name]}</span>&amp;#x27;</span>&quot;,&quot;&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>def</span> <span class=\&quot;hljs-title function_\&quot;>get_style_image_markdown_text</span>(<span class=\&quot;hljs-params\&quot;>style_name: <span class=\&quot;hljs-built_in\&quot;>str</span></span>) -&amp;gt; <span class=\&quot;hljs-built_in\&quot;>str</span>:&quot;,&quot;    url = get_style_image_path(style_name)&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>return</span> <span class=\&quot;hljs-string\&quot;>f&amp;#x27;&amp;lt;img id=&amp;quot;style-image&amp;quot; src=&amp;quot;<span class=\&quot;hljs-subst\&quot;>{url}</span>&amp;quot; alt=&amp;quot;style image&amp;quot;&amp;gt;&amp;#x27;</span>&quot;,&quot;&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>def</span> <span class=\&quot;hljs-title function_\&quot;>update_style_image</span>(<span class=\&quot;hljs-params\&quot;>style_name: <span class=\&quot;hljs-built_in\&quot;>str</span></span>) -&amp;gt; <span class=\&quot;hljs-built_in\&quot;>dict</span>:&quot;,&quot;    text = get_style_image_markdown_text(style_name)&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>return</span> gr.Markdown.update(value=text)&quot;,&quot;&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>def</span> <span class=\&quot;hljs-title function_\&quot;>inference</span>(<span class=\&quot;hljs-params\&quot;>img, vid</span>):&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>if</span> <span class=\&quot;hljs-keyword\&quot;>not</span> os.path.exists(<span class=\&quot;hljs-string\&quot;>&amp;#x27;temp&amp;#x27;</span>):&quot;,&quot;        os.system(<span class=\&quot;hljs-string\&quot;>&amp;#x27;mkdir temp&amp;#x27;</span>)&quot;,&quot;&quot;,&quot;    img.save(<span class=\&quot;hljs-string\&quot;>&amp;quot;temp/image.jpg&amp;quot;</span>, <span class=\&quot;hljs-string\&quot;>&amp;quot;JPEG&amp;quot;</span>)&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>if</span> torch.cuda.is_available():&quot;,&quot;        os.system(<span class=\&quot;hljs-string\&quot;>f&amp;quot;python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image &amp;#x27;temp/image.jpg&amp;#x27; --driving_video <span class=\&quot;hljs-subst\&quot;>{vid}</span> --result_video &amp;#x27;./temp/result.mp4&amp;#x27;&amp;quot;</span>)&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>else</span>:&quot;,&quot;        os.system(<span class=\&quot;hljs-string\&quot;>f&amp;quot;python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image &amp;#x27;temp/image.jpg&amp;#x27; --driving_video <span class=\&quot;hljs-subst\&quot;>{vid}</span> --result_video &amp;#x27;./temp/result.mp4&amp;#x27; --cpu&amp;quot;</span>)&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>return</span> <span class=\&quot;hljs-string\&quot;>&amp;#x27;./temp/result.mp4&amp;#x27;</span>&quot;,&quot;&quot;,&quot;&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>def</span> <span class=\&quot;hljs-title function_\&quot;>main</span>():&quot;,&quot;    <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Blocks(css=<span class=\&quot;hljs-string\&quot;>&amp;#x27;style.css&amp;#x27;</span>) <span class=\&quot;hljs-keyword\&quot;>as</span> demo:&quot;,&quot;        gr.Markdown(title)&quot;,&quot;        gr.Markdown(DESCRIPTION)&quot;,&quot;&quot;,&quot;        <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Box():&quot;,&quot;            gr.Markdown(<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;## Step 1 (Provide Input Face Image)</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>- Drop an image containing a face to the **Input Image**.</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>    - If there are multiple faces in the image, use Edit button in the upper right corner and crop the input image beforehand.</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;</span>)&quot;,&quot;            <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Column():&quot;,&quot;                    <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                        input_image = gr.Image(label=<span class=\&quot;hljs-string\&quot;>&amp;#x27;Input Image&amp;#x27;</span>,&quot;,&quot;                                               <span class=\&quot;hljs-built_in\&quot;>type</span>=<span class=\&quot;hljs-string\&quot;>&amp;quot;pil&amp;quot;</span>)&quot;,&quot;&quot;,&quot;            <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                paths = <span class=\&quot;hljs-built_in\&quot;>sorted</span>(pathlib.Path(<span class=\&quot;hljs-string\&quot;>&amp;#x27;assets&amp;#x27;</span>).glob(<span class=\&quot;hljs-string\&quot;>&amp;#x27;*.png&amp;#x27;</span>))&quot;,&quot;                gr.Examples(inputs=[input_image],&quot;,&quot;                            examples=[[path.as_posix()] <span class=\&quot;hljs-keyword\&quot;>for</span> path <span class=\&quot;hljs-keyword\&quot;>in</span> paths])&quot;,&quot;&quot;,&quot;        <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Box():&quot;,&quot;            gr.Markdown(<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;## Step 2 (Select Driving Video)</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>- Select **Style Driving Video for the face image animation**.</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;</span>)&quot;,&quot;            <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Column():&quot;,&quot;                    <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                        driving_video = gr.Video(label=<span class=\&quot;hljs-string\&quot;>&amp;#x27;Driving Video&amp;#x27;</span>,&quot;,&quot;                                                 <span class=\&quot;hljs-built_in\&quot;>format</span>=<span class=\&quot;hljs-string\&quot;>&amp;quot;mp4&amp;quot;</span>)&quot;,&quot;&quot;,&quot;            <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                paths = <span class=\&quot;hljs-built_in\&quot;>sorted</span>(pathlib.Path(<span class=\&quot;hljs-string\&quot;>&amp;#x27;assets&amp;#x27;</span>).glob(<span class=\&quot;hljs-string\&quot;>&amp;#x27;*.mp4&amp;#x27;</span>))&quot;,&quot;                gr.Examples(inputs=[driving_video],&quot;,&quot;                            examples=[[path.as_posix()] <span class=\&quot;hljs-keyword\&quot;>for</span> path <span class=\&quot;hljs-keyword\&quot;>in</span> paths])&quot;,&quot;&quot;,&quot;        <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Box():&quot;,&quot;            gr.Markdown(<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;## Step 3 (Generate Animated Image based on the Video)</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>- Hit the **Generate** button. (Note: On cpu-basic, it takes ~ 10 minutes to generate final results.)</span>&quot;,&quot;<span class=\&quot;hljs-string\&quot;>&amp;#x27;&amp;#x27;&amp;#x27;</span>)&quot;,&quot;            <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Column():&quot;,&quot;                    <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Row():&quot;,&quot;                        generate_button = gr.Button(<span class=\&quot;hljs-string\&quot;>&amp;#x27;Generate&amp;#x27;</span>)&quot;,&quot;&quot;,&quot;                <span class=\&quot;hljs-keyword\&quot;>with</span> gr.Column():&quot;,&quot;                    result = gr.Video(label=<span class=\&quot;hljs-string\&quot;>&amp;quot;Output&amp;quot;</span>)&quot;,&quot;        gr.Markdown(FOOTER)&quot;,&quot;        generate_button.click(fn=inference,&quot;,&quot;                              inputs=[&quot;,&quot;                                  input_image,&quot;,&quot;                                  driving_video&quot;,&quot;                              ],&quot;,&quot;                              outputs=result)&quot;,&quot;&quot;,&quot;    demo.queue(max_size=<span class=\&quot;hljs-number\&quot;>10</span>).launch()&quot;,&quot;&quot;,&quot;<span class=\&quot;hljs-keyword\&quot;>if</span> __name__ == <span class=\&quot;hljs-string\&quot;>&amp;#x27;__main__&amp;#x27;</span>:&quot;,&quot;    main()&quot;,&quot;&quot;],&quot;context&quot;:{&quot;repo&quot;:{&quot;name&quot;:&quot;CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model&quot;,&quot;type&quot;:&quot;space&quot;},&quot;revision&quot;:&quot;626d7ffc7b07ba96290bd80cb09b73993f85bf40&quot;,&quot;path&quot;:&quot;app.py&quot;}}" data-target="BlobContent">
+
+<div class="relative text-sm"><div class="overflow-x-auto"><table class="min-w-full border-collapse font-mono"><tbody><tr class="" id="L1">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="1"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> os<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L2">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="2"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> pathlib<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L3">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="3"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L4">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="4"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> gradio <span class="hljs-keyword">as</span> gr<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L5">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="5"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">import</span> torch<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L6">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="6"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">from</span> PIL <span class="hljs-keyword">import</span> Image<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L7">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="7"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L8">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="8"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->repo_dir = pathlib.Path(<span class="hljs-string">&quot;Thin-Plate-Spline-Motion-Model&quot;</span>).absolute()<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L9">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="9"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> repo_dir.exists():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L10">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="10"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    os.system(<span class="hljs-string">&quot;git clone https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L11">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="11"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->os.chdir(repo_dir.name)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L12">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="12"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> (repo_dir / <span class="hljs-string">&quot;checkpoints&quot;</span>).exists():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L13">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="13"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    os.system(<span class="hljs-string">&quot;mkdir checkpoints&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L14">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="14"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> (repo_dir / <span class="hljs-string">&quot;checkpoints/vox.pth.tar&quot;</span>).exists():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L15">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="15"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    os.system(<span class="hljs-string">&quot;gdown 1-CKOjv_y_TzNe-dwQsjjeVxJUuyBAb5X -O checkpoints/vox.pth.tar&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L16">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="16"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L17">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="17"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L18">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="18"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L19">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="19"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->title = <span class="hljs-string">&quot;# Thin-Plate Spline Motion Model for Image Animation&quot;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L20">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="20"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->DESCRIPTION = <span class="hljs-string">&#x27;&#x27;&#x27;### Gradio demo for &lt;b&gt;Thin-Plate Spline Motion Model for Image Animation&lt;/b&gt;, CVPR 2022. &lt;a href=&#x27;https://arxiv.org/abs/2203.14367&#x27;&gt;[Paper]&lt;/a&gt;&lt;a href=&#x27;https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model&#x27;&gt;[Github Code]&lt;/a&gt;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L21">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="21"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string"></span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L22">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="22"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">&lt;img id=&quot;overview&quot; alt=&quot;overview&quot; src=&quot;https://github.com/yoyo-nb/Thin-Plate-Spline-Motion-Model/raw/main/assets/vox.gif&quot; /&gt;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L23">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="23"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">&#x27;&#x27;&#x27;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L24">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="24"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->FOOTER = <span class="hljs-string">&#x27;&lt;img id=&quot;visitor-badge&quot; alt=&quot;visitor badge&quot; src=&quot;https://visitor-badge.glitch.me/badge?page_id=gradio-blocks.Image-Animation-using-Thin-Plate-Spline-Motion-Model&quot; /&gt;&#x27;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L25">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="25"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L26">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="26"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L27">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="27"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">get_style_image_path</span>(<span class="hljs-params">style_name: <span class="hljs-built_in">str</span></span>) -&gt; <span class="hljs-built_in">str</span>:<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L28">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="28"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    base_path = <span class="hljs-string">&#x27;assets&#x27;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L29">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="29"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    filenames = {<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L30">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="30"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        <span class="hljs-string">&#x27;source&#x27;</span>: <span class="hljs-string">&#x27;source.png&#x27;</span>,<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L31">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="31"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        <span class="hljs-string">&#x27;driving&#x27;</span>: <span class="hljs-string">&#x27;driving.mp4&#x27;</span>,<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L32">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="32"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    }<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L33">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="33"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">return</span> <span class="hljs-string">f&#x27;<span class="hljs-subst">{base_path}</span>/<span class="hljs-subst">{filenames[style_name]}</span>&#x27;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L34">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="34"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L35">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="35"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L36">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="36"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">get_style_image_markdown_text</span>(<span class="hljs-params">style_name: <span class="hljs-built_in">str</span></span>) -&gt; <span class="hljs-built_in">str</span>:<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L37">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="37"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    url = get_style_image_path(style_name)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L38">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="38"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">return</span> <span class="hljs-string">f&#x27;&lt;img id=&quot;style-image&quot; src=&quot;<span class="hljs-subst">{url}</span>&quot; alt=&quot;style image&quot;&gt;&#x27;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L39">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="39"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L40">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="40"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L41">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="41"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">update_style_image</span>(<span class="hljs-params">style_name: <span class="hljs-built_in">str</span></span>) -&gt; <span class="hljs-built_in">dict</span>:<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L42">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="42"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    text = get_style_image_markdown_text(style_name)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L43">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="43"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">return</span> gr.Markdown.update(value=text)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L44">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="44"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L45">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="45"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L46">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="46"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">inference</span>(<span class="hljs-params">img, vid</span>):<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L47">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="47"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">if</span> <span class="hljs-keyword">not</span> os.path.exists(<span class="hljs-string">&#x27;temp&#x27;</span>):<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L48">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="48"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        os.system(<span class="hljs-string">&#x27;mkdir temp&#x27;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L49">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="49"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L50">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="50"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    img.save(<span class="hljs-string">&quot;temp/image.jpg&quot;</span>, <span class="hljs-string">&quot;JPEG&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L51">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="51"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">if</span> torch.cuda.is_available():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L52">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="52"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        os.system(<span class="hljs-string">f&quot;python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image &#x27;temp/image.jpg&#x27; --driving_video <span class="hljs-subst">{vid}</span> --result_video &#x27;./temp/result.mp4&#x27;&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L53">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="53"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">else</span>:<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L54">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="54"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        os.system(<span class="hljs-string">f&quot;python demo.py --config config/vox-256.yaml --checkpoint ./checkpoints/vox.pth.tar --source_image &#x27;temp/image.jpg&#x27; --driving_video <span class="hljs-subst">{vid}</span> --result_video &#x27;./temp/result.mp4&#x27; --cpu&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L55">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="55"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">return</span> <span class="hljs-string">&#x27;./temp/result.mp4&#x27;</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L56">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="56"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L57">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="57"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L58">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="58"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L59">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="59"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-keyword">def</span> <span class="hljs-title function_">main</span>():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L60">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="60"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->    <span class="hljs-keyword">with</span> gr.Blocks(css=<span class="hljs-string">&#x27;style.css&#x27;</span>) <span class="hljs-keyword">as</span> demo:<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L61">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="61"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        gr.Markdown(title)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L62">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="62"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        gr.Markdown(DESCRIPTION)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L63">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="63"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L64">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="64"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        <span class="hljs-keyword">with</span> gr.Box():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L65">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="65"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            gr.Markdown(<span class="hljs-string">&#x27;&#x27;&#x27;## Step 1 (Provide Input Face Image)</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L66">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="66"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">- Drop an image containing a face to the **Input Image**.</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L67">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="67"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">    - If there are multiple faces in the image, use Edit button in the upper right corner and crop the input image beforehand.</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L68">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="68"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">&#x27;&#x27;&#x27;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L69">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="69"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L70">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="70"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                <span class="hljs-keyword">with</span> gr.Column():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L71">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="71"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                    <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L72">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="72"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                        input_image = gr.Image(label=<span class="hljs-string">&#x27;Input Image&#x27;</span>,<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L73">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="73"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                                               <span class="hljs-built_in">type</span>=<span class="hljs-string">&quot;pil&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L74">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="74"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L75">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="75"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L76">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="76"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                paths = <span class="hljs-built_in">sorted</span>(pathlib.Path(<span class="hljs-string">&#x27;assets&#x27;</span>).glob(<span class="hljs-string">&#x27;*.png&#x27;</span>))<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L77">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="77"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                gr.Examples(inputs=[input_image],<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L78">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="78"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                            examples=[[path.as_posix()] <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> paths])<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L79">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="79"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L80">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="80"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        <span class="hljs-keyword">with</span> gr.Box():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L81">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="81"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            gr.Markdown(<span class="hljs-string">&#x27;&#x27;&#x27;## Step 2 (Select Driving Video)</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L82">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="82"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">- Select **Style Driving Video for the face image animation**.</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L83">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="83"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">&#x27;&#x27;&#x27;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L84">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="84"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L85">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="85"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                <span class="hljs-keyword">with</span> gr.Column():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L86">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="86"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                    <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L87">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="87"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                        driving_video = gr.Video(label=<span class="hljs-string">&#x27;Driving Video&#x27;</span>,<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L88">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="88"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                                                 <span class="hljs-built_in">format</span>=<span class="hljs-string">&quot;mp4&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L89">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="89"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L90">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="90"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L91">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="91"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                paths = <span class="hljs-built_in">sorted</span>(pathlib.Path(<span class="hljs-string">&#x27;assets&#x27;</span>).glob(<span class="hljs-string">&#x27;*.mp4&#x27;</span>))<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L92">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="92"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                gr.Examples(inputs=[driving_video],<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L93">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="93"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                            examples=[[path.as_posix()] <span class="hljs-keyword">for</span> path <span class="hljs-keyword">in</span> paths])<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L94">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="94"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L95">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="95"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        <span class="hljs-keyword">with</span> gr.Box():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L96">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="96"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            gr.Markdown(<span class="hljs-string">&#x27;&#x27;&#x27;## Step 3 (Generate Animated Image based on the Video)</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L97">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="97"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">- Hit the **Generate** button. (Note: On cpu-basic, it takes ~ 10 minutes to generate final results.)</span><!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L98">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="98"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START --><span class="hljs-string">&#x27;&#x27;&#x27;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L99">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="99"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->            <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L100">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="100"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                <span class="hljs-keyword">with</span> gr.Column():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L101">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="101"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                    <span class="hljs-keyword">with</span> gr.Row():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L102">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="102"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                        generate_button = gr.Button(<span class="hljs-string">&#x27;Generate&#x27;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L103">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="103"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->
+<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L104">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="104"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                <span class="hljs-keyword">with</span> gr.Column():<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L105">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="105"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                    result = gr.Video(label=<span class="hljs-string">&quot;Output&quot;</span>)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L106">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="106"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        gr.Markdown(FOOTER)<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L107">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="107"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->        generate_button.click(fn=inference,<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L108">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="108"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                              inputs=[<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L109">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="109"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->                                  input_image,<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L110">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="110"></td>
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packages.txt

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+		<div class="mb-2 flex items-center overflow-hidden"><a class="truncate text-gray-800 hover:underline" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/tree/main">Image-Animation-using-Thin-Plate-Spline-Motion-Model</a>
+			<span class="mx-1 text-gray-300">/</span>
+				<span class="dark:text-gray-300">packages.txt</span></div></div>
+
+	
+	</header>
+			<div class="SVELTE_HYDRATER contents" data-props="{&quot;commitLast&quot;:{&quot;date&quot;:&quot;2022-06-20T17:29:50.000Z&quot;,&quot;subject&quot;:&quot;Create new file&quot;,&quot;authors&quot;:[{&quot;_id&quot;:&quot;605b1a536ce6cabbb3474b5a&quot;,&quot;avatar&quot;:&quot;https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1655583590216-605b1a536ce6cabbb3474b5a.jpeg?w=200&amp;h=200&amp;f=face&quot;,&quot;isHf&quot;:false,&quot;user&quot;:&quot;animesh007&quot;}],&quot;commit&quot;:{&quot;id&quot;:&quot;73b9008f8d04a987e527dde27eb26fd62e2b63d0&quot;,&quot;parentIds&quot;:[&quot;452fabb89c69438a23fb71c11f008fb39f6a0e44&quot;]},&quot;title&quot;:&quot;Create new file&quot;},&quot;repo&quot;:{&quot;name&quot;:&quot;CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model&quot;,&quot;type&quot;:&quot;space&quot;}}" data-target="LastCommit"><div class="from-gray-100-to-white flex items-baseline rounded-t-lg border border-b-0 bg-gradient-to-t px-3 py-2 dark:border-gray-800"><img class="mr-2.5 mt-0.5 h-4 w-4 self-center rounded-full" alt="animesh007's picture" src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1655583590216-605b1a536ce6cabbb3474b5a.jpeg?w=200&amp;h=200&amp;f=face">
+			<div class="mr-5 flex flex-none items-center truncate"><a class="hover:underline" href="/animesh007">animesh007
+					</a>
+				
+			</div>
+		<div class="mr-4 truncate font-mono text-sm text-gray-500 hover:prose-a:underline"><!-- HTML_TAG_START -->Create new file<!-- HTML_TAG_END --></div>
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+		
+		<time class="ml-auto hidden flex-none truncate pl-2 text-gray-500 dark:text-gray-400 lg:block" datetime="2022-06-20T17:29:50" title="Mon, 20 Jun 2022 17:29:50 GMT">over 1 year ago</time></div></div>
+			<div class="flex flex-wrap items-center border px-3 py-1.5 text-sm text-gray-800 dark:border-gray-800 dark:bg-gray-900">
+				<a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/raw/main/packages.txt"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z" fill="currentColor"></path><path d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z" fill="currentColor"></path><path d="M12.419 25.484L17.639 6l1.932.518L14.35 26z" fill="currentColor"></path></svg>
+							raw
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+							history
+						</a><a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/blame/main/packages.txt"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M16 2a14 14 0 1 0 14 14A14 14 0 0 0 16 2zm0 26a12 12 0 1 1 12-12a12 12 0 0 1-12 12z" fill="currentColor"></path><path d="M11.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path><path d="M20.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path></svg>
+							blame
+						</a><a class="my-1 mr-4 flex items-center hover:underline text-green-600 dark:text-gray-300" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/edit/main/packages.txt"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M2 26h28v2H2z" fill="currentColor"></path><path d="M25.4 9c.8-.8.8-2 0-2.8l-3.6-3.6c-.8-.8-2-.8-2.8 0l-15 15V24h6.4l15-15zm-5-5L24 7.6l-3 3L17.4 7l3-3zM6 22v-3.6l10-10l3.6 3.6l-10 10H6z" fill="currentColor"></path></svg>
+							contribute
+						</a><a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/delete/main/packages.txt"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M12 12h2v12h-2z" fill="currentColor"></path><path d="M18 12h2v12h-2z" fill="currentColor"></path><path d="M4 6v2h2v20a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V8h2V6zm4 22V8h16v20z" fill="currentColor"></path><path d="M12 2h8v2h-8z" fill="currentColor"></path></svg>
+							delete
+						</a>
+				<div class="mr-4 flex items-center text-gray-400"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
+
+							No virus
+						</div>
+				
+				<div class="dark:text-gray-300 sm:ml-auto">23 Bytes</div></div>
+
+			<div class="relative min-h-[100px] rounded-b-lg border border-t-0 leading-tight dark:border-gray-800 dark:bg-gray-925">
+				<div class="py-3"><div class="SVELTE_HYDRATER contents" data-props="{&quot;lines&quot;:[&quot;bzip2&quot;,&quot;cmake&quot;,&quot;ninja-build&quot;],&quot;context&quot;:{&quot;repo&quot;:{&quot;name&quot;:&quot;CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model&quot;,&quot;type&quot;:&quot;space&quot;},&quot;revision&quot;:&quot;626d7ffc7b07ba96290bd80cb09b73993f85bf40&quot;,&quot;path&quot;:&quot;packages.txt&quot;}}" data-target="BlobContent">
+
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+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="1"></td>
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+						
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+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->ninja-build<!-- HTML_TAG_END --></td>
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+	</div></div></div></div></section></div></main>
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requirements.txt

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+<!DOCTYPE html>
+<html class="">
+	<head>
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+		<script type="text/javascript" src="https://de5282c3ca0c.edge.sdk.awswaf.com/de5282c3ca0c/526cf06acb0d/challenge.js" defer></script>
+	</head>
+	<body class="flex flex-col min-h-screen bg-white dark:bg-gray-950 text-black ViewerBlobPage">
+		<div class="flex min-h-screen flex-col">
+	<div class="SVELTE_HYDRATER contents" data-props="{&quot;classNames&quot;:&quot;&quot;,&quot;isWide&quot;:false,&quot;isZh&quot;:false}" data-target="MainHeader"><header class="border-b border-gray-100 "><div class="w-full px-4 container flex h-16 items-center"><div class="flex flex-1 items-center"><a class="mr-5 flex flex-none items-center lg:mr-6" href="/"><img alt="Hugging Face's logo" class="w-7 md:mr-2" src="/front/assets/huggingface_logo-noborder.svg">
+				<span class="hidden whitespace-nowrap text-lg font-bold md:block">Hugging Face</span></a>
+			<div class="relative flex-1 lg:max-w-sm mr-2 sm:mr-4 lg:mr-6"><input autocomplete="off" class="w-full dark:bg-gray-950 pl-8 form-input-alt h-9 pr-3 focus:shadow-xl" name="" placeholder="Search models, datasets, users..."  spellcheck="false" type="text" value="">
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+
+	</div></div>
+		<nav aria-label="Main" class="ml-auto hidden lg:block"><ul class="flex items-center space-x-2"><li><a class="group flex items-center px-2 py-0.5 dark:hover:text-gray-400 hover:text-indigo-700" href="/models"><svg class="mr-1.5 text-gray-400 group-hover:text-indigo-500" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 24 24"><path class="uim-quaternary" d="M20.23 7.24L12 12L3.77 7.24a1.98 1.98 0 0 1 .7-.71L11 2.76c.62-.35 1.38-.35 2 0l6.53 3.77c.29.173.531.418.7.71z" opacity=".25" fill="currentColor"></path><path class="uim-tertiary" d="M12 12v9.5a2.09 2.09 0 0 1-.91-.21L4.5 17.48a2.003 2.003 0 0 1-1-1.73v-7.5a2.06 2.06 0 0 1 .27-1.01L12 12z" opacity=".5" fill="currentColor"></path><path class="uim-primary" d="M20.5 8.25v7.5a2.003 2.003 0 0 1-1 1.73l-6.62 3.82c-.275.13-.576.198-.88.2V12l8.23-4.76c.175.308.268.656.27 1.01z" fill="currentColor"></path></svg>
+					Models</a>
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+		<div class="mb-2 flex items-center overflow-hidden"><a class="truncate text-gray-800 hover:underline" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/tree/main">Image-Animation-using-Thin-Plate-Spline-Motion-Model</a>
+			<span class="mx-1 text-gray-300">/</span>
+				<span class="dark:text-gray-300">requirements.txt</span></div></div>
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+	</header>
+			<div class="SVELTE_HYDRATER contents" data-props="{&quot;commitLast&quot;:{&quot;date&quot;:&quot;2023-10-25T19:06:23.000Z&quot;,&quot;subject&quot;:&quot;Fix dependency issues and add some updates (#10)&quot;,&quot;authors&quot;:[{&quot;_id&quot;:&quot;605b1a536ce6cabbb3474b5a&quot;,&quot;avatar&quot;:&quot;https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1655583590216-605b1a536ce6cabbb3474b5a.jpeg?w=200&amp;h=200&amp;f=face&quot;,&quot;isHf&quot;:false,&quot;user&quot;:&quot;animesh007&quot;}],&quot;commit&quot;:{&quot;id&quot;:&quot;626d7ffc7b07ba96290bd80cb09b73993f85bf40&quot;,&quot;parentIds&quot;:[&quot;6614b1ace6e25e6d94942514c1e9b6e9d3942f46&quot;]},&quot;title&quot;:&quot;Fix dependency issues and add some updates (<a href=\&quot;/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/discussions/10\&quot;>#10</a>)&quot;},&quot;repo&quot;:{&quot;name&quot;:&quot;CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model&quot;,&quot;type&quot;:&quot;space&quot;}}" data-target="LastCommit"><div class="from-gray-100-to-white flex items-baseline rounded-t-lg border border-b-0 bg-gradient-to-t px-3 py-2 dark:border-gray-800"><img class="mr-2.5 mt-0.5 h-4 w-4 self-center rounded-full" alt="animesh007's picture" src="https://aeiljuispo.cloudimg.io/v7/https://cdn-uploads.huggingface.co/production/uploads/1655583590216-605b1a536ce6cabbb3474b5a.jpeg?w=200&amp;h=200&amp;f=face">
+			<div class="mr-5 flex flex-none items-center truncate"><a class="hover:underline" href="/animesh007">animesh007
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+		<div class="mr-4 truncate font-mono text-sm text-gray-500 hover:prose-a:underline"><!-- HTML_TAG_START -->Fix dependency issues and add some updates (<a href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/discussions/10">#10</a>)<!-- HTML_TAG_END --></div>
+		<a class="rounded border bg-gray-50 px-1.5 text-sm hover:underline dark:border-gray-800 dark:bg-gray-900" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/commit/626d7ffc7b07ba96290bd80cb09b73993f85bf40">626d7ff</a>
+		
+		<time class="ml-auto hidden flex-none truncate pl-2 text-gray-500 dark:text-gray-400 lg:block" datetime="2023-10-25T19:06:23" title="Wed, 25 Oct 2023 19:06:23 GMT">16 days ago</time></div></div>
+			<div class="flex flex-wrap items-center border px-3 py-1.5 text-sm text-gray-800 dark:border-gray-800 dark:bg-gray-900">
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+							blame
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+							contribute
+						</a><a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/delete/main/requirements.txt"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M12 12h2v12h-2z" fill="currentColor"></path><path d="M18 12h2v12h-2z" fill="currentColor"></path><path d="M4 6v2h2v20a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V8h2V6zm4 22V8h16v20z" fill="currentColor"></path><path d="M12 2h8v2h-8z" fill="currentColor"></path></svg>
+							delete
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+				<div class="mr-4 flex items-center text-gray-400"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
+
+							No virus
+						</div>
+				
+				<div class="dark:text-gray-300 sm:ml-auto">1.34 kB</div></div>
+
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+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="1"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->aiofiles==23.2.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L2">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="2"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->altair==5.1.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L3">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="3"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->annotated-types==0.6.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L4">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="4"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->anyio==3.7.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L5">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="5"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->attrs==23.1.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L6">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="6"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->beautifulsoup4==4.12.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L7">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="7"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->certifi==2023.7.22<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L8">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="8"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->charset-normalizer==3.3.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L9">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="9"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->click==8.1.7<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L10">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="10"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->cmake==3.27.7<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L11">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="11"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->contourpy==1.1.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L12">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="12"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->cycler==0.12.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L13">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="13"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->exceptiongroup==1.1.3<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L14">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="14"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->fastapi==0.103.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L15">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="15"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->ffmpy==0.3.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L16">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="16"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->filelock==3.12.4<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L17">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="17"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->fonttools==4.43.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L18">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="18"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->fsspec==2023.9.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L19">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="19"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->gdown==4.7.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L20">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="20"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->gradio==3.48.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L21">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="21"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->gradio_client==0.6.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L22">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="22"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->h11==0.14.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L23">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="23"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->httpcore==0.18.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L24">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="24"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->httpx==0.25.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L25">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="25"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->huggingface-hub==0.18.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L26">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="26"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->idna==3.4<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L27">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="27"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->imageio==2.31.5<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L28">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="28"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->imageio-ffmpeg==0.4.9<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L29">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="29"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->importlib-resources==6.1.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L30">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="30"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->Jinja2==3.1.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L31">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="31"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->jsonschema==4.19.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L32">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="32"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->jsonschema-specifications==2023.7.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L33">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="33"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->kiwisolver==1.4.5<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L34">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="34"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->lazy_loader==0.3<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L35">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="35"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->lit==17.0.3<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L36">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="36"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->MarkupSafe==2.1.3<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L37">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="37"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->matplotlib==3.8.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L38">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="38"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->mpmath==1.3.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L39">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="39"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->networkx==3.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L40">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="40"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->numpy==1.25.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L41">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="41"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->orjson==3.9.9<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L42">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="42"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->packaging==23.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L43">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="43"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->pandas==2.1.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L44">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="44"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->Pillow==10.1.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L45">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="45"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->psutil==5.9.6<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L46">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="46"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->pydantic==2.4.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L47">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="47"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->pydantic_core==2.10.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L48">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="48"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->pydub==0.25.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L49">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="49"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->pyparsing==3.1.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L50">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="50"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->PySocks==1.7.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L51">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="51"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->python-dateutil==2.8.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L52">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="52"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->python-multipart==0.0.6<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L53">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="53"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->pytz==2023.3.post1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L54">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="54"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->PyYAML==6.0.1<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L55">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="55"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->referencing==0.30.2<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L56">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="56"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->requests==2.31.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L57">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="57"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->rpds-py==0.10.6<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L58">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="58"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->scikit-image==0.22.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L59">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="59"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->scipy==1.11.3<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L60">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="60"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->semantic-version==2.10.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L61">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="61"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->setuptools-scm==8.0.4<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L62">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="62"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->six==1.16.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L63">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="63"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->sniffio==1.3.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L64">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="64"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->soupsieve==2.5<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L65">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="65"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->starlette==0.27.0<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L66">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="66"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->sympy==1.12<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L67">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="67"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->tifffile==2023.9.26<!-- HTML_TAG_END --></td>
+					</tr><tr class="" id="L68">
+						
+						<td class="blob-line-num w-1 cursor-pointer select-none pl-5 pr-3 text-right text-gray-300 hover:text-black" data-line-num="68"></td>
+						<td class="overflow-visible whitespace-pre px-3"><!-- HTML_TAG_START -->tomli==2.0.1<!-- HTML_TAG_END --></td>
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+		<div class="mb-2 flex items-center overflow-hidden"><a class="truncate text-gray-800 hover:underline" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/tree/main">Image-Animation-using-Thin-Plate-Spline-Motion-Model</a>
+			<span class="mx-1 text-gray-300">/</span>
+				<span class="dark:text-gray-300">style.css</span></div></div>
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+			<div class="mr-5 flex flex-none items-center truncate"><a class="hover:underline" href="/animesh007">animesh007
+					</a>
+				
+			</div>
+		<div class="mr-4 truncate font-mono text-sm text-gray-500 hover:prose-a:underline"><!-- HTML_TAG_START -->Create new file<!-- HTML_TAG_END --></div>
+		<a class="rounded border bg-gray-50 px-1.5 text-sm hover:underline dark:border-gray-800 dark:bg-gray-900" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/commit/80691d374a3a292e95e3493d74d5e64672092b87">80691d3</a>
+		
+		<time class="ml-auto hidden flex-none truncate pl-2 text-gray-500 dark:text-gray-400 lg:block" datetime="2022-06-20T17:20:56" title="Mon, 20 Jun 2022 17:20:56 GMT">over 1 year ago</time></div></div>
+			<div class="flex flex-wrap items-center border px-3 py-1.5 text-sm text-gray-800 dark:border-gray-800 dark:bg-gray-900">
+				<a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/raw/main/style.css"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M31 16l-7 7l-1.41-1.41L28.17 16l-5.58-5.59L24 9l7 7z" fill="currentColor"></path><path d="M1 16l7-7l1.41 1.41L3.83 16l5.58 5.59L8 23l-7-7z" fill="currentColor"></path><path d="M12.419 25.484L17.639 6l1.932.518L14.35 26z" fill="currentColor"></path></svg>
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+							history
+						</a><a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/blame/main/style.css"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32" style="transform: rotate(360deg);"><path d="M16 2a14 14 0 1 0 14 14A14 14 0 0 0 16 2zm0 26a12 12 0 1 1 12-12a12 12 0 0 1-12 12z" fill="currentColor"></path><path d="M11.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path><path d="M20.5 11a2.5 2.5 0 1 0 2.5 2.5a2.48 2.48 0 0 0-2.5-2.5z" fill="currentColor"></path></svg>
+							blame
+						</a><a class="my-1 mr-4 flex items-center hover:underline text-green-600 dark:text-gray-300" href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/edit/main/style.css"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M2 26h28v2H2z" fill="currentColor"></path><path d="M25.4 9c.8-.8.8-2 0-2.8l-3.6-3.6c-.8-.8-2-.8-2.8 0l-15 15V24h6.4l15-15zm-5-5L24 7.6l-3 3L17.4 7l3-3zM6 22v-3.6l10-10l3.6 3.6l-10 10H6z" fill="currentColor"></path></svg>
+							contribute
+						</a><a class="my-1 mr-4 flex items-center hover:underline " href="/spaces/CVPR/Image-Animation-using-Thin-Plate-Spline-Motion-Model/delete/main/style.css"><svg class="mr-1.5" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" aria-hidden="true" focusable="false" role="img" width="1em" height="1em" preserveAspectRatio="xMidYMid meet" viewBox="0 0 32 32"><path d="M12 12h2v12h-2z" fill="currentColor"></path><path d="M18 12h2v12h-2z" fill="currentColor"></path><path d="M4 6v2h2v20a2 2 0 0 0 2 2h16a2 2 0 0 0 2-2V8h2V6zm4 22V8h16v20z" fill="currentColor"></path><path d="M12 2h8v2h-8z" fill="currentColor"></path></svg>
+							delete
+						</a>
+				<div class="mr-4 flex items-center text-gray-400"><svg class="text-gray-300 text-sm mr-1.5 -translate-y-px" width="1em" height="1em" viewBox="0 0 22 28" fill="none" xmlns="http://www.w3.org/2000/svg"><path fill-rule="evenodd" clip-rule="evenodd" d="M15.3634 10.3639C15.8486 10.8491 15.8486 11.6357 15.3634 12.1209L10.9292 16.5551C10.6058 16.8785 10.0814 16.8785 9.7579 16.5551L7.03051 13.8277C6.54532 13.3425 6.54532 12.5558 7.03051 12.0707C7.51569 11.5855 8.30234 11.5855 8.78752 12.0707L9.7579 13.041C10.0814 13.3645 10.6058 13.3645 10.9292 13.041L13.6064 10.3639C14.0916 9.8787 14.8782 9.8787 15.3634 10.3639Z" fill="currentColor"></path><path fill-rule="evenodd" clip-rule="evenodd" d="M10.6666 27.12C4.93329 25.28 0 19.2267 0 12.7867V6.52001C0 5.40001 0.693334 4.41334 1.73333 4.01334L9.73333 1.01334C10.3333 0.786673 11 0.786673 11.6 1.02667L19.6 4.02667C20.1083 4.21658 20.5465 4.55701 20.8562 5.00252C21.1659 5.44803 21.3324 5.97742 21.3333 6.52001V12.7867C21.3333 19.24 16.4 25.28 10.6666 27.12Z" fill="currentColor" fill-opacity="0.22"></path><path d="M10.0845 1.94967L10.0867 1.94881C10.4587 1.8083 10.8666 1.81036 11.2286 1.95515L11.2387 1.95919L11.2489 1.963L19.2489 4.963L19.25 4.96342C19.5677 5.08211 19.8416 5.29488 20.0351 5.57333C20.2285 5.85151 20.3326 6.18203 20.3333 6.52082C20.3333 6.52113 20.3333 6.52144 20.3333 6.52176L20.3333 12.7867C20.3333 18.6535 15.8922 24.2319 10.6666 26.0652C5.44153 24.2316 1 18.6409 1 12.7867V6.52001C1 5.82357 1.42893 5.20343 2.08883 4.94803L10.0845 1.94967Z" stroke="currentColor" stroke-opacity="0.30" stroke-width="2"></path></svg>
+
+							No virus
+						</div>
+				
+				<div class="dark:text-gray-300 sm:ml-auto">273 Bytes</div></div>
+
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