added windows installation doc

README.md

@@ -45,7 +45,9 @@ ECON is designed for "Human digitization from a color image", which combines the
 
 ## News :triangular_flag_on_post:
 
-- [2022/12/22] <a href='https://colab.research.google.com/drive/1YRgwoRCZIrSB2e7auEWFyG10Xzjbrbno?usp=sharing' style='padding-left: 0.5rem;'><img src='https://colab.research.google.com/assets/colab-badge.svg' alt='Google Colab'></a> is now available, created by [AroArz](https://github.com/AroArz)!
+- [2023/01/06] [Justin John](https://github.com/justinjohn0306) and [
+Carlos Barreto](https://github.com/carlosedubarreto) creates [install-on-windows](docs/installation-windows.md) for ECON .
+- [2022/12/22] <a href='https://colab.research.google.com/drive/1YRgwoRCZIrSB2e7auEWFyG10Xzjbrbno?usp=sharing' style='padding-left: 0.5rem;'><img src='https://colab.research.google.com/assets/colab-badge.svg' alt='Google Colab'></a> is now available, created by [Aron Arzoomand](https://github.com/AroArz).
 - [2022/12/15] Both <a href="#demo">demo</a> and <a href="https://arxiv.org/abs/2212.07422">arXiv</a> are available.
 
 ## TODO
@@ -68,9 +70,6 @@ ECON is designed for "Human digitization from a color image", which combines the
     <li>
       <a href="#applications">Applications</a>
     </li>
-    <li>
-      <a href="#tricks">Tricks</a>
-    </li>
     <li>
       <a href="#citation">Citation</a>
     </li>
@@ -81,49 +80,26 @@ ECON is designed for "Human digitization from a color image", which combines the
 
 ## Instructions
 
-- See [docs/installation.md](docs/installation.md) to install all the required packages and setup the models
+- See [installion doc for Windows](docs/installation-windows.md) to install all the required packages and setup the models on _Windows_
+- See [installion doc for Ubuntu](docs/installation-ubuntu.md) to install all the required packages and setup the models on _Ubuntu_
+- See [magic tricks](docs/tricks.md) to know a few technical tricks to further improve and accelerate ECON
 
 ## Demo
 
 ```bash
-# For single-person image-based reconstruction (w/ all visualization steps, 1.8min)
+# For single-person image-based reconstruction (w/ l visualization steps, 1.8min)
 python -m apps.infer -cfg ./configs/econ.yaml -in_dir ./examples -out_dir ./results
 
-# For single-person image-based reconstruction (w/o any visualization steps, 1.5min)
-python -m apps.infer -cfg ./configs/econ.yaml -in_dir ./examples -out_dir ./results -novis
-
 # For multi-person image-based reconstruction (see config/econ.yaml)
 python -m apps.infer -cfg ./configs/econ.yaml -in_dir ./examples -out_dir ./results -multi
 
 # To generate the demo video of reconstruction results
-python -m apps.multi_render -n {filename}
+python -m apps.multi_render -n <filename>
 
 # To animate the reconstruction with SMPL-X pose parameters
-python -m apps.avatarizer -n {filename}
+python -m apps.avatarizer -n <filename>
 ```
 
-## Tricks
-
-### Some adjustable parameters in _config/econ.yaml_
-
-- `use_ifnet: False`
-  - True: use IF-Nets+ for mesh completion ( $\text{ECON}_\text{IF}$ - Better quality, **~2min / img**)
-  - False: use SMPL-X for mesh completion ( $\text{ECON}_\text{EX}$ - Faster speed, **~1.8min / img**)
-- `use_smpl: ["hand", "face"]`
-  - [ ]: don't use either hands or face parts from SMPL-X
-  - ["hand"]: only use the **visible** hands from SMPL-X
-  - ["hand", "face"]: use both **visible** hands and face from SMPL-X
-- `thickness: 2cm`
-  - could be increased accordingly in case final reconstruction **xx_full.obj** looks flat
-- `k: 4`
-  - could be reduced accordingly in case the surface of **xx_full.obj** has discontinous artifacts
-- `hps_type: PIXIE`
-  - "pixie": more accurate for face and hands
-  - "pymafx": more robust for challenging poses
-- `texture_src: image`
-  - "image": direct mapping the aligned pixels to final mesh
-  - "SD": use Stable Diffusion to generate full texture (TODO)
-
 <br/>
 
 ## More Qualitative Results
@@ -176,6 +152,17 @@ Some images used in the qualitative examples come from [pinterest.com](https://w
 
 This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No.860768 ([CLIPE Project](https://www.clipe-itn.eu)).
 
+
+## Contributors
+
+Kudos to all of our amazing contributors! ECON thrives through open-source. In that spirit, we welcome all kinds of contributions from the community.
+
+<a href="https://github.com/yuliangxiu/ECON/graphs/contributors">
+  <img src="https://contrib.rocks/image?repo=yuliangxiu/ECON" />
+</a>
+
+_Contributor avatars are randomly shuffled._
+
 ---
 
 <br>

docs/installation-windows.md

@@ -0,0 +1,108 @@
+# Windows installation tutorial
+
+Another [issue#16](https://github.com/YuliangXiu/ECON/issues/16) shows the whole process to deploy ECON on *Windows*
+
+## Dependencies and Installation
+
+- Use [Anaconda](https://www.anaconda.com/products/distribution)
+- NVIDIA GPU + [CUDA](https://developer.nvidia.com/cuda-downloads)
+- [Wget for Windows](https://eternallybored.org/misc/wget/1.21.3/64/wget.exe)
+- Create a new folder on your C drive and rename it "wget" and move the downloaded "wget.exe" over there.
+- Add the path to your wget folder to your system environment variables at `Environment Variables > System Variables Path > Edit environment variable`
+
+![image](https://user-images.githubusercontent.com/34035011/210986038-39dbb7a1-12ef-4be9-9af4-5f658c6beb65.png)
+
+- Install [Git for Windows 64-bit](https://git-scm.com/download/win)
+- [Visual Studio Community 2022](https://visualstudio.microsoft.com/) (Make sure to check all the boxes as shown in the image below)
+
+![image](https://user-images.githubusercontent.com/34035011/210983023-4e5a0024-68f0-4adb-8089-6ff598aec220.PNG)
+
+
+
+## Getting started
+
+Start by cloning the repo:
+
+```bash
+git clone https://github.com/yuliangxiu/ECON.git
+cd ECON
+```
+
+## Environment
+
+- Windows 10 / 11
+- **CUDA=11.4**
+- Python = 3.8
+- PyTorch >= 1.12.1 (official [Get Started](https://pytorch.org/get-started/locally/))
+- Cupy >= 11.3.0 (offcial [Installation](https://docs.cupy.dev/en/stable/install.html#installing-cupy-from-pypi))
+- PyTorch3D (official [INSTALL.md](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md), recommend [install-from-local-clone](https://github.com/facebookresearch/pytorch3d/blob/main/INSTALL.md#2-install-from-a-local-clone))
+
+```bash
+# install required packages
+cd ECON
+conda env create -f environment-windows.yaml
+conda activate econ
+
+# install pytorch and cupy
+pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113
+pip install -r requirements-win.txt
+pip install cupy-cuda11x
+
+## If you have a RTX 30 series GPU then run this cmd below for installing neural_voxelization_layer
+pip install git+https://github.com/YuliangXiu/neural_voxelization_layer.git
+## If you have GPU below RTX 30 series then you gotta build neural_voxelization_layer (steps below)
+git clone https://github.com/justinjohn0306/neural_voxelization_layer.git
+
+cd neural_voxelization_layer
+python setup install
+cd..
+
+# install libmesh & libvoxelize
+cd lib/common/libmesh
+python setup.py build_ext --inplace
+cd ../libvoxelize
+python setup.py build_ext --inplace
+```
+
+## Register at [ICON's website](https://icon.is.tue.mpg.de/)
+
+![Register](../assets/register.png)
+Required:
+
+- [SMPL](http://smpl.is.tue.mpg.de/): SMPL Model (Male, Female)
+- [SMPL-X](http://smpl-x.is.tue.mpg.de/): SMPL-X Model, used for training
+- [SMPLIFY](http://smplify.is.tue.mpg.de/): SMPL Model (Neutral)
+- [PIXIE](https://icon.is.tue.mpg.de/user.php): PIXIE SMPL-X estimator
+
+:warning: Click **Register now** on all dependencies, then you can download them all with **ONE** account.
+
+## Downloading required models and extra data (make sure to install git and wget for windows for this to work)
+
+```bash
+cd ECON
+bash fetch_data.sh # requires username and password
+```
+
+## Citation
+
+:+1: Please consider citing these awesome HPS approaches: PyMAF-X, PIXIE
+
+
+```
+@article{pymafx2022,
+  title={PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images},
+  author={Zhang, Hongwen and Tian, Yating and Zhang, Yuxiang and Li, Mengcheng and An, Liang and Sun, Zhenan and Liu, Yebin},
+  journal={arXiv preprint arXiv:2207.06400},
+  year={2022}
+}
+
+
+@inproceedings{PIXIE:2021,
+  title={Collaborative Regression of Expressive Bodies using Moderation},
+  author={Yao Feng and Vasileios Choutas and Timo Bolkart and Dimitrios Tzionas and Michael J. Black},
+  booktitle={International Conference on 3D Vision (3DV)},
+  year={2021}
+}
+
+
+```

docs/tricks.md

@@ -0,0 +1,29 @@
+## Technical tricks to improve or accelerate ECON
+
+### If the reconstructed geometry is not satisfying, play with the adjustable parameters in _config/econ.yaml_
+
+- `use_smpl: ["hand", "face"]`
+  - [ ]: don't use either hands or face parts from SMPL-X
+  - ["hand"]: only use the **visible** hands from SMPL-X
+  - ["hand", "face"]: use both **visible** hands and face from SMPL-X
+- `thickness: 2cm`
+  - could be increased accordingly in case final reconstruction **xx_full.obj** looks flat
+- `k: 4`
+  - could be reduced accordingly in case the surface of **xx_full.obj** has discontinous artifacts
+- `hps_type: PIXIE`
+  - "pixie": more accurate for face and hands
+  - "pymafx": more robust for challenging poses
+- `texture_src: image`
+  - "image": direct mapping the aligned pixels to final mesh
+  - "SD": use Stable Diffusion to generate full texture (TODO)
+
+### To accelerate the inference, you could
+
+- `use_ifnet: False`
+  - True: use IF-Nets+ for mesh completion ( $\text{ECON}_\text{IF}$ - Better quality, **~2min / img**)
+  - False: use SMPL-X for mesh completion ( $\text{ECON}_\text{EX}$ - Faster speed, **~1.8min / img**)
+
+```bash
+# For single-person image-based reconstruction (w/o all visualization steps, 1.5min)
+python -m apps.infer -cfg ./configs/econ.yaml -in_dir ./examples -out_dir ./results -novis
+```

environment-windows.yaml

@@ -0,0 +1,16 @@
+name: econ
+channels:
+  - nvidia
+  - conda-forge
+  - fvcore
+  - iopath
+  - bottler
+  - defaults
+dependencies:
+  - python=3.8
+  - fvcore
+  - iopath
+  - cupy
+  - cython
+  - pip
+

requirements-win.txt

@@ -0,0 +1,19 @@
+matplotlib
+scikit-image
+trimesh
+rtree
+pytorch_lightning
+kornia>0.4.0
+chumpy
+opencv-python
+opencv_contrib_python
+scikit-learn
+protobuf
+dataclasses
+mediapipe
+einops
+boto3
+open3d
+tinyobjloader==2.0.0rc7
+git+https://github.com/facebookresearch/pytorch3d.git
+git+https://github.com