# SPDX-FileCopyrightText: Copyright (c) 2021-2022 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: LicenseRef-NvidiaProprietary # # NVIDIA CORPORATION, its affiliates and licensors retain all intellectual # property and proprietary rights in and to this material, related # documentation and any modifications thereto. Any use, reproduction, # disclosure or distribution of this material and related documentation # without an express license agreement from NVIDIA CORPORATION or # its affiliates is strictly prohibited. from os import device_encoding from turtle import update import math import torch import numpy as np import torch.nn.functional as F import cv2 import torchvision from torch_utils import persistence from recon.models.stylegannext3D.networks_stylegan2_new import Generator as StyleGAN2Backbone_cond from recon.volumetric_rendering.renderer import ImportanceRenderer, ImportanceRenderer_bsMotion from recon.volumetric_rendering.ray_sampler import RaySampler, RaySampler_zxc import dnnlib from recon.volumetric_rendering.renderer import fill_mouth @persistence.persistent_class class TriPlaneGenerator(torch.nn.Module): def __init__(self, z_dim, # Input latent (Z) dimensionality. c_dim, # Conditioning label (C) dimensionality. w_dim, # Intermediate latent (W) dimensionality. img_resolution, # Output resolution. img_channels, # Number of output color channels. topology_path=None, # sr_num_fp16_res=0, mapping_kwargs={}, # Arguments for MappingNetwork. rendering_kwargs={}, sr_kwargs={}, **synthesis_kwargs, # Arguments for SynthesisNetwork. ): super().__init__() self.z_dim = z_dim self.c_dim = c_dim self.w_dim = w_dim self.img_resolution = img_resolution self.img_channels = img_channels self.renderer = ImportanceRenderer_bsMotion() self.ray_sampler = RaySampler_zxc() self.texture_backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32, mapping_kwargs=mapping_kwargs, **synthesis_kwargs) # render neural texture self.face_backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32, mapping_kwargs=mapping_kwargs, **synthesis_kwargs) self.backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32 * 3, mapping_ws=self.texture_backbone.num_ws, mapping_kwargs=mapping_kwargs, **synthesis_kwargs) self.superresolution = dnnlib.util.construct_class_by_name(class_name='recon.models.stylegannext3D.superresolution.SuperresolutionHybrid8XDC', channels=32, img_resolution=img_resolution, sr_num_fp16_res=sr_num_fp16_res, sr_antialias=rendering_kwargs['sr_antialias'], **sr_kwargs) self.decoder = OSGDecoder(32, {'decoder_lr_mul': rendering_kwargs.get('decoder_lr_mul', 1), 'decoder_output_dim': 32}) self.neural_rendering_resolution = 128 self.rendering_kwargs = rendering_kwargs self.fill_mouth = True def mapping(self, z, c, truncation_psi=1, truncation_cutoff=None, update_emas=False): if self.rendering_kwargs['c_gen_conditioning_zero']: c = torch.zeros_like(c) c = c[:, :self.c_dim] # remove expression labels return self.backbone.mapping(z, c * self.rendering_kwargs.get('c_scale', 0), truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) def visualize_mesh_condition(self, mesh_condition, to_imgs=False): uvcoords_image = mesh_condition['uvcoords_image'].clone().permute(0, 3, 1, 2) # [B, C, H, W] ori_alpha_image = uvcoords_image[:, 2:].clone() full_alpha_image, mouth_masks = fill_mouth(ori_alpha_image, blur_mouth_edge=False) # upper_mouth_mask = mouth_masks.clone() # upper_mouth_mask[:, :, :87] = 0 # alpha_image = torch.clamp(ori_alpha_image + upper_mouth_mask, min=0, max=1) if to_imgs: uvcoords_image[full_alpha_image.expand(-1, 3, -1, -1) == 0] = -1 uvcoords_image = ((uvcoords_image+1)*127.5).to(dtype=torch.uint8).cpu() vis_images = [] for vis_uvcoords in uvcoords_image: vis_images.append(torchvision.transforms.ToPILImage()(vis_uvcoords)) return vis_images else: return uvcoords_image def synthesis(self, ws, c, mesh_condition, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, return_featmap=False, evaluation=False, **synthesis_kwargs): batch_size = ws.shape[0] # cam = c[:, -25:] cam = c cam2world_matrix = cam[:, :16].view(-1, 4, 4) intrinsics = cam[:, 16:25].view(-1, 3, 3) if neural_rendering_resolution is None: neural_rendering_resolution = self.neural_rendering_resolution else: self.neural_rendering_resolution = neural_rendering_resolution # print(self.neural_rendering_resolution) # Create a batch of rays for volume rendering ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution) # Create triplanes by running StyleGAN backbone N, M, _ = ray_origins.shape texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_plane = static_feats[-1] static_plane = static_plane.view(len(static_plane), 3, 32, static_plane.shape[-2], static_plane.shape[-1]) static_feats[0] = static_feats[0].view(len(static_plane), 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0] static_feats[-1] = static_plane[:, 0] assert len(static_feats) == len(texture_feats) bbox_256 = [57, 185, 64, 192] # the face region is the center-crop result from the frontal triplane. rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition , static_feats, bbox_256) rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ # blend features of neural texture and tri-plane full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) rendering_stitch = rendering_stitch.view(*static_plane.shape) blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) # Perform volume rendering if evaluation: assert 'noise_mode' in synthesis_kwargs.keys() and synthesis_kwargs['noise_mode'] == 'const', \ ('noise_mode' in synthesis_kwargs.keys(), synthesis_kwargs['noise_mode'] == 'const') feature_samples, depth_samples, weights_samples = self.renderer(blended_planes, self.decoder, ray_origins, ray_directions, self.rendering_kwargs, evaluation=evaluation) # Reshape into 'raw' neural-rendered image H = W = self.neural_rendering_resolution feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous() depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W) # Run superresolution to get final image rgb_image = feature_image[:, :3] # rgb_image = weights_samples * rgb_image + (1 - weights_samples) * torch.ones_like(rgb_image) sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], **{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'}) if return_featmap: return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image, 'image_feature': feature_image, 'triplane': blended_planes, 'texture': texture_feats, 'static_plane': static_plane, 'rendering_stitch': rendering_stitch}#static_plane, 'texture_map': texture_feats[-2]} else: return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image} def synthesis_withTexture(self, ws, texture_feats, c, mesh_condition, static_feats=None, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, evaluation=False, **synthesis_kwargs): bs = ws.shape[0] # eg3d_ws, texture_ws = ws[:, :self.texture_backbone.num_ws], ws[:, self.texture_backbone.num_ws:] # cam = c[:, :25] cam = c[:, -25:] cam2world_matrix = cam[:, :16].view(-1, 4, 4) intrinsics = cam[:, 16:25].view(-1, 3, 3) if neural_rendering_resolution is None: neural_rendering_resolution = self.neural_rendering_resolution else: self.neural_rendering_resolution = neural_rendering_resolution # Create a batch of rays for volume rendering ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution) # Create triplanes by running StyleGAN backbone N, M, _ = ray_origins.shape if static_feats is None: static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_plane = static_feats[-1].view(bs, 3, 32, static_feats[-1].shape[-2], static_feats[-1].shape[-1]) assert len(static_feats) == len(texture_feats), (len(static_feats), len(texture_feats)) bbox_256 = [57, 185, 64, 192] rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], bbox_256=bbox_256, static_feats=[static_feats[0].view(bs, 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0]] + static_feats[1:-1] + [static_plane[:, 0]]) rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) # upper_mouth_mask = mouth_masks.clone() # upper_mouth_mask[:, :, :87] = 0 # rendering_stitch = F.interpolate(static_plane[:, 0, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]], size=(256, 256), mode='bilinear', # antialias=True) * upper_mouth_mask + rendering_stitch * (1 - upper_mouth_mask) rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ # blend features of neural texture and tri-plane full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) rendering_stitch = rendering_stitch.view(*static_plane.shape) blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) # if flag is not False: # import cv2 # with torch.no_grad(): # if not hasattr(self, 'weight'): # self.weight = torch.nn.Conv2d(32, 3, 1).weight.cuda() # weight = self.weight # vis = torch.nn.functional.conv2d((rendering_stitch * full_alpha_image)[:, 0, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]], weight) # max_ = [torch.max(torch.abs(vis[:, i])) for i in range(3)] # for i in range(3): vis[:, i] /= max_[i] # print('rendering_stitch', vis.max().item(), vis.min().item()) # vis = torch.cat([vis[i] for i in range(blended_planes.shape[0])], dim=-1) # vis = (vis.permute(1, 2, 0).clamp(min=-1.0, max=1.0) + 1.) * 127.5 # cv2.imwrite('vis_%s_rendering_stitch.png' % flag, vis.cpu().numpy().astype(np.uint8)[..., ::-1]) # vis = torch.nn.functional.conv2d((static_plane * (1 - full_alpha_image))[:, 0], weight) # for i in range(3): vis[:, i] /= max_[i] # print('static_plane', vis.max().item(), vis.min().item()) # vis = torch.cat([vis[i] for i in range(blended_planes.shape[0])], dim=-1) # vis = (vis.permute(1, 2, 0).clamp(min=-1.0, max=1.0) + 1.) * 127.5 # cv2.imwrite('vis_%s_static_plane.png' % flag, vis.cpu().numpy().astype(np.uint8)[..., ::-1]) # vis = torch.nn.functional.conv2d(blended_planes[:, 0], weight) # for i in range(3): vis[:, i] /= max_[i] # print('blended_planes', vis.max().item(), vis.min().item()) # vis = torch.cat([vis[i] for i in range(blended_planes.shape[0])], dim=-1) # vis = (vis.permute(1, 2, 0).clamp(min=-1.0, max=1.0) + 1.) * 127.5 # cv2.imwrite('vis_%s_blended_planes.png' % flag, vis.cpu().numpy().astype(np.uint8)[..., ::-1]) # Perform volume rendering if evaluation: assert 'noise_mode' in synthesis_kwargs.keys() and synthesis_kwargs['noise_mode']=='const',\ ('noise_mode' in synthesis_kwargs.keys(), synthesis_kwargs['noise_mode']=='const') feature_samples, depth_samples, weights_samples = self.renderer(blended_planes, self.decoder, ray_origins, ray_directions, self.rendering_kwargs, evaluation=evaluation) # Reshape into 'raw' neural-rendered image H = W = self.neural_rendering_resolution feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous() depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W) # Run superresolution to get final image rgb_image = feature_image[:, :3] sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], **{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'}) return {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image, 'feature_image': feature_image, 'triplane': blended_planes}#static_plane, 'texture_map': texture_feats[-2]} def synthesis_withCondition(self, ws, c, mesh_condition, gt_texture_feats=None, gt_static_feats=None, texture_feats_conditions=None, static_feats_conditions=None, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, only_image=False, return_feats=False, **synthesis_kwargs): bs = ws.shape[0] cam = c[:, -25:] cam2world_matrix = cam[:, :16].view(-1, 4, 4) intrinsics = cam[:, 16:25].view(-1, 3, 3) if neural_rendering_resolution is None: neural_rendering_resolution = self.neural_rendering_resolution else: self.neural_rendering_resolution = neural_rendering_resolution # Create a batch of rays for volume rendering ray_origins, ray_directions = self.ray_sampler(cam2world_matrix, intrinsics, neural_rendering_resolution) # Create triplanes by running StyleGAN backbone N, M, _ = ray_origins.shape if gt_texture_feats is None: texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, feat_conditions=texture_feats_conditions, update_emas=update_emas, **synthesis_kwargs) if gt_static_feats is None: static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, feat_conditions=static_feats_conditions, update_emas=update_emas, **synthesis_kwargs) static_plane = static_feats[-1].view(bs, 3, 32, static_feats[-1].shape[-2], static_feats[-1].shape[-1]) assert len(static_feats) == len(texture_feats) bbox_256 = [57, 185, 64, 192] rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], bbox_256=bbox_256, static_feats=[static_feats[0].view(bs, 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0]] + static_feats[1:-1] + [static_plane[:, 0]]) rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ # blend features of neural texture and tri-plane full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) rendering_stitch = rendering_stitch.view(*static_plane.shape) blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) # Perform volume rendering evaluation = 'noise_mode' in synthesis_kwargs.keys() and synthesis_kwargs['noise_mode']=='const' feature_samples, depth_samples, weights_samples = self.renderer(blended_planes, self.decoder, ray_origins, ray_directions, self.rendering_kwargs, evaluation=evaluation) # Reshape into 'raw' neural-rendered image H = W = self.neural_rendering_resolution feature_image = feature_samples.permute(0, 2, 1).reshape(N, feature_samples.shape[-1], H, W).contiguous() depth_image = depth_samples.permute(0, 2, 1).reshape(N, 1, H, W) # Run superresolution to get final image rgb_image = feature_image[:, :3] sr_image = self.superresolution(rgb_image, feature_image, ws, noise_mode=self.rendering_kwargs['superresolution_noise_mode'], **{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if k != 'noise_mode'}) if only_image: return {'image': sr_image} out = {'image': sr_image, 'image_raw': rgb_image, 'image_depth': depth_image, 'feature_image': feature_image, 'triplane': blended_planes} if return_feats: out['static'] = static_feats out['texture'] = texture_feats return out def rasterize(self, texture_feats, uvcoords_image, static_feats, bbox_256): ''' uvcoords_image [B, H, W, C] ''' if not uvcoords_image.dtype == torch.float32: uvcoords_image = uvcoords_image.float() grid, alpha_image = uvcoords_image[..., :2], uvcoords_image[..., 2:].permute(0, 3, 1, 2) full_alpha_image, mouth_masks = fill_mouth(alpha_image.clone(), blur_mouth_edge=False) upper_mouth_mask = mouth_masks.clone() upper_mouth_mask[:, :, :87] = 0 upper_mouth_alpha_image = torch.clamp(alpha_image + upper_mouth_mask, min=0, max=1) rendering_images = [] for idx, texture in enumerate(texture_feats): res = texture.shape[2] bbox = [round(i * res / 256) for i in bbox_256] rendering_image = F.grid_sample(texture, grid, align_corners=False) rendering_feat = F.interpolate(rendering_image, size=(res, res), mode='bilinear', antialias=True) alpha_image_ = F.interpolate(alpha_image, size=(res, res), mode='bilinear', antialias=True) static_feat = F.interpolate(static_feats[idx][:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]], size=(res, res), mode='bilinear', antialias=True) rendering_images.append(torch.cat([ rendering_feat * alpha_image_ + static_feat * (1 - alpha_image_), F.interpolate(upper_mouth_alpha_image, size=(res, res), mode='bilinear', antialias=True)], dim=1)) # print('rendering_images', grid.shape, rendering_images[-1].shape) return rendering_images, full_alpha_image, mouth_masks def sample(self, coordinates, directions, z, c, mesh_condition, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): # Compute RGB features, density for arbitrary 3D coordinates. Mostly used for extracting shapes. ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) batch_size = ws.shape[0] texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_plane = static_feats[-1] static_plane = static_plane.view(len(static_plane), 3, 32, static_plane.shape[-2], static_plane.shape[-1]) static_feats[0] = static_feats[0].view(len(static_plane), 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0] static_feats[-1] = static_plane[:, 0] assert len(static_feats) == len(texture_feats) bbox_256 = [57, 185, 64, 192] rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], static_feats, bbox_256) rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ # blend features of neural texture and tri-plane full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) rendering_stitch = rendering_stitch.view(*static_plane.shape) blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) return self.renderer.run_model(blended_planes, self.decoder, coordinates, directions, self.rendering_kwargs) def sample_mixed(self, coordinates, directions, ws, mesh_condition, truncation_psi=1, truncation_cutoff=None, update_emas=False, **synthesis_kwargs): # Same as sample, but expects latent vectors 'ws' instead of Gaussian noise 'z' batch_size = ws.shape[0] texture_feats = self.texture_backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_feats = self.backbone.synthesis(ws, cond_list=None, return_list=True, update_emas=update_emas, **synthesis_kwargs) static_plane = static_feats[-1] static_plane = static_plane.view(len(static_plane), 3, 32, static_plane.shape[-2], static_plane.shape[-1]) static_feats[0] = static_feats[0].view(len(static_plane), 3, 32, static_feats[0].shape[-2], static_feats[0].shape[-1])[:, 0] static_feats[-1] = static_plane[:, 0] assert len(static_feats) == len(texture_feats) bbox_256 = [57, 185, 64, 192] rendering_images, full_alpha_image, mouth_masks = self.rasterize(texture_feats, mesh_condition['uvcoords_image'], static_feats, bbox_256) rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False, update_emas=update_emas, **synthesis_kwargs) rendering_stitch_, full_alpha_image_ = torch.zeros_like(rendering_stitch), torch.zeros_like(full_alpha_image) rendering_stitch_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(rendering_stitch, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image_[:, :, bbox_256[0]:bbox_256[1], bbox_256[2]:bbox_256[3]] = F.interpolate(full_alpha_image, size=(128, 128), mode='bilinear', antialias=True) full_alpha_image, rendering_stitch = full_alpha_image_, rendering_stitch_ # blend features of neural texture and tri-plane full_alpha_image = torch.cat((full_alpha_image, torch.zeros_like(full_alpha_image), torch.zeros_like(full_alpha_image)), 1).unsqueeze(2) rendering_stitch = torch.cat((rendering_stitch, torch.zeros_like(rendering_stitch), torch.zeros_like(rendering_stitch)), 1) rendering_stitch = rendering_stitch.view(*static_plane.shape) blended_planes = rendering_stitch * full_alpha_image + static_plane * (1 - full_alpha_image) return self.renderer.run_model(blended_planes, self.decoder, coordinates, directions, self.rendering_kwargs) def forward(self, z, c, v, truncation_psi=1, truncation_cutoff=None, neural_rendering_resolution=None, update_emas=False, cache_backbone=False, use_cached_backbone=False, **synthesis_kwargs): # Render a batch of generated images. ws = self.mapping(z, c, truncation_psi=truncation_psi, truncation_cutoff=truncation_cutoff, update_emas=update_emas) return self.synthesis(ws, c, v, update_emas=update_emas, neural_rendering_resolution=neural_rendering_resolution, cache_backbone=cache_backbone, use_cached_backbone=use_cached_backbone, **synthesis_kwargs) from training.networks_stylegan2 import FullyConnectedLayer class OSGDecoder(torch.nn.Module): def __init__(self, n_features, options): super().__init__() self.hidden_dim = 64 self.net = torch.nn.Sequential( FullyConnectedLayer(n_features, self.hidden_dim, lr_multiplier=options['decoder_lr_mul']), torch.nn.Softplus(), FullyConnectedLayer(self.hidden_dim, 1 + options['decoder_output_dim'], lr_multiplier=options['decoder_lr_mul']) ) def forward(self, sampled_features, ray_directions, sampled_embeddings=None): # Aggregate features sampled_features = sampled_features.mean(1) x = sampled_features N, M, C = x.shape x = x.view(N * M, C) x = self.net(x) x = x.view(N, M, -1) rgb = torch.sigmoid(x[..., 1:]) * (1 + 2 * 0.001) - 0.001 # Uses sigmoid clamping from MipNeRF sigma = x[..., 0:1] return {'rgb': rgb, 'sigma': sigma}