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# 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.
import torch
import torch.nn.functional as F
import dnnlib
from torch_utils import persistence
from einops import rearrange
from recon.training.generator.triplane_v20_original import OSGDecoder
from recon.training.reconstructor.networks_reconstructor import EncoderGlobal, EncoderDetail, EncoderCanonical, \
DecoderTriplane
from recon.training.reconstructor.triplane_ae import Encoder as TriEncoder
from recon.volumetric_rendering.renderer import ImportanceRenderer, ImportanceRenderer_bsMotion
from recon.volumetric_rendering.ray_sampler import RaySampler, RaySampler_zxc
from recon.volumetric_rendering.renderer import fill_mouth
from Next3d.training_avatar_texture.networks_stylegan2_new import Generator as StyleGAN2Backbone_cond
# Animatable triplane reconstructor Psi in Portrait4D
@persistence.persistent_class
class TriPlaneReconstructorNeutralize(torch.nn.Module):
def __init__(self,
img_resolution=512,
mot_dims=512,
w_dim=512,
sr_num_fp16_res=0,
has_background=False,
has_superresolution=True,
flame_full=True,
masked_sampling=False,
num_blocks_neutral=4,
num_blocks_motion=4,
motion_map_layers=2,
neural_rendering_resolution=64,
deformation_kwargs={},
rendering_kwargs={},
sr_kwargs={},
encoder_pre_weights=None,
**synthesis_kwargs, # Arguments for SynthesisNetwork.
):
super().__init__()
self.mot_dims = mot_dims
self.motion_map_layers = motion_map_layers
self.encoder_global = EncoderGlobal(encoder_weights=encoder_pre_weights)
self.encoder_detail = EncoderDetail()
self.encoder_global_latent_tri = TriEncoder(n_hiddens=64, image_channel=32, z_channels=128,
downsample=[4, 4, 4])
self.encoder_canonical = EncoderCanonical(num_blocks_neutral=num_blocks_neutral,
num_blocks_motion=num_blocks_motion, mot_dims=mot_dims,
mapping_layers=motion_map_layers)
self.generator_triplane = DecoderTriplane()
self.renderer = ImportanceRenderer_bsMotion()
self.ray_sampler = RaySampler_zxc()
decoder_output_dim = 32 if has_superresolution else 3
self.superresolution = dnnlib.util.construct_class_by_name(class_name=rendering_kwargs['superresolution_module'], channels=32,
img_resolution=img_resolution, sr_num_fp16_res=sr_num_fp16_res,
sr_antialias=rendering_kwargs['sr_antialias'], **sr_kwargs)
self.has_superresolution = True
self.img_resolution = img_resolution
#
# if self.has_superresolution:
# superres_module_name = rendering_kwargs['superresolution_module'].replace('training.superresolution',
# 'models.stylegan.superresolution')
# self.superresolution = dnnlib.util.construct_class_by_name(class_name=superres_module_name, channels=32,
# img_resolution=img_resolution,
# sr_num_fp16_res=sr_num_fp16_res,
# sr_antialias=rendering_kwargs['sr_antialias'],
# **sr_kwargs)
# else:
# self.superresolution = None
self.decoder = OSGDecoder(32, {'decoder_lr_mul': rendering_kwargs.get('decoder_lr_mul', 1),
'decoder_output_dim': decoder_output_dim})
self.neural_rendering_resolution = neural_rendering_resolution
self.rendering_kwargs = rendering_kwargs
z_dim = 512
w_dim = 512
c_dim = 25
synthesis_kwargs = {'channel_base': 32768, 'channel_max': 512, 'fused_modconv_default': 'inference_only', 'num_fp16_res': 0,
'conv_clamp': None}
mapping_kwargs = {'num_layers': 2}
self.face_backbone = StyleGAN2Backbone_cond(z_dim, c_dim, w_dim, img_resolution=256, img_channels=32,
mapping_kwargs= mapping_kwargs, use_tanh=False,
**synthesis_kwargs)
self.triplnae_encoder = EncoderTriplane()
def synthesis(self, imgs_app, imgs_mot, motions_app, motions, c, mesh, latent_recon, triplane_recon, ws_avg,
neural_rendering_resolution=None,
use_cached_backbone=False, motion_scale=1.0, **synthesis_kwargs):
triplane_recon_input = self.get_triplane(ws_avg, triplane_recon, mesh)
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
if use_cached_backbone and self._last_planes is not None:
planes = self._last_planes
else:
features_global = self.encoder_global(imgs_app)
features_detail = self.encoder_detail(imgs_app)
cano_tri_ref = rearrange(triplane_recon_input, "b f c h w -> b c f h w")
cano_global = self.encoder_global_latent_tri(cano_tri_ref)
cano_global = rearrange( cano_global, "b c f h w -> b (c f) h w")
features_canonical = self.encoder_canonical(features_global, cano_global, motions, motions_app,
scale=motion_scale)
features_canonical_lr = features_canonical[0]
features_canonical_sr = features_canonical[1]
triplane_recon_ref = rearrange(triplane_recon_input, "b f c h w -> b (f c) h w")
planes = self.generator_triplane(features_canonical_sr, features_detail, triplane_recon_ref)
planes = planes.view(len(planes), -1, 32, planes.shape[-2], planes.shape[-1])
# Reshape output into three 32-channel planes
# if not isinstance(planes, list):
# planes = [planes]
# planes = [p.view(len(p), -1, 32, p.shape[-2], p.shape[-1]) for p in planes]
feature_samples, depth_samples, weights_samples = self.renderer(planes, self.decoder, ray_origins,
ray_directions,
self.rendering_kwargs, evaluation=False)
# 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]
ws_avg = ws_avg.repeat(rgb_image.shape[0], 1, 1)
sr_image = self.superresolution(rgb_image, feature_image, ws_avg,
noise_mode=self.rendering_kwargs['superresolution_noise_mode'],
**{k: synthesis_kwargs[k] for k in synthesis_kwargs.keys() if
k != 'noise_mode'})
out = {'image_sr': sr_image, 'image': rgb_image, 'image_depth': depth_image,
'image_feature': feature_image, 'triplane': planes}
return out # static_plane, 'texture_map': texture_feats[-2]}
def rasterize_sinle_input(self, texture_feat_input, uvcoords_image, static_feat_input, bbox_256,
res_list=[32, 32, 64, 128, 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)
res = texture_feat_input.shape[2]
bbox = [round(i * res / 256) for i in bbox_256]
rendering_image = F.grid_sample(texture_feat_input, 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_feat_input[:, :, bbox[0]:bbox[1], bbox[2]:bbox[3]], size=(res, res),
mode='bilinear', antialias=True)
condition_mask_list = []
rendering_img_nomask = rendering_feat * alpha_image_ + static_feat * (1 - alpha_image_)
rendering_image = torch.cat([
rendering_img_nomask,
F.interpolate(upper_mouth_alpha_image, size=(res, res), mode='bilinear', antialias=True)], dim=1)
for res_mask in res_list:
condition_mask = F.interpolate(upper_mouth_alpha_image, size=(res_mask, res_mask), mode='bilinear',
antialias=True)
condition_mask_list.append(condition_mask)
# print('rendering_images', grid.shape, rendering_images[-1].shape)
return rendering_image, full_alpha_image, rendering_img_nomask, condition_mask_list
def get_triplane(self, ws, triplane, mesh_condition):
b = triplane.shape[0]
ws = ws.repeat(b, 1, 1)
# Create a batch of rays for volume rendering
# Create triplanes by running StyleGAN backbone
static_plane = triplane[:, 1:, :, :, :]
static_plane_face = static_plane[:, 0]
bbox_256 = [57, 185, 64, 192] # the face region is the center-crop result from the frontal triplane.
texture_feat = triplane[:, 0:1, :, :, :].squeeze(1)
rendering_image, full_alpha_image, rendering_image_only_img, mask_images = self.rasterize_sinle_input(
texture_feat,
mesh_condition,
static_plane_face,
bbox_256
)
rendering_images_no_masks = self.triplnae_encoder(rendering_image)
rendering_images = []
for index, rendering_image_no_mask in enumerate(rendering_images_no_masks):
rendering_images_each = torch.cat([rendering_image_no_mask, mask_images[index]], dim=1)
rendering_images.append(rendering_images_each)
rendering_images.append(rendering_image)
rendering_stitch = self.face_backbone.synthesis(ws, rendering_images, return_list=False )
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 blended_planes
def sample_mixed(self, imgs_app, imgs_mot, mesh, ws_avg, motions_app, motions, coordinates, directions, latent_recon,
triplane_recon, motion_scale=1.0, **synthesis_kwargs):
triplane_recon_input = self.get_triplane(ws_avg, triplane_recon, mesh)
features_global = self.encoder_global(imgs_app)
features_detail = self.encoder_detail(imgs_app)
cano_tri_ref = rearrange(triplane_recon_input, "b f c h w -> b c f h w")
cano_global = self.encoder_global_latent_tri(cano_tri_ref)
cano_global = rearrange(cano_global, "b c f h w -> b (c f) h w")
features_canonical = self.encoder_canonical(features_global, cano_global, motions, motions_app,
scale=motion_scale)
features_canonical_lr = features_canonical[0]
features_canonical_sr = features_canonical[1]
triplane_recon_ref = rearrange(triplane_recon_input, "b f c h w -> b (f c) h w")
planes = self.generator_triplane(features_canonical_sr, features_detail, triplane_recon_ref)
planes = planes.view(len(planes), -1, 32, planes.shape[-2], planes.shape[-1])
return self.renderer.run_model(planes, self.decoder, coordinates, directions, self.rendering_kwargs)
def forward(self, imgs_app, imgs_mot, motions_app, motions,
c, mesh, triplane_recon, ws_avg, neural_rendering_resolution=None, motion_scale=1.0, **synthesis_kwargs):
img_dict = self.synthesis(imgs_app, imgs_mot, motions_app, motions, c, mesh, triplane_recon, triplane_recon, ws_avg,
neural_rendering_resolution=neural_rendering_resolution,
motion_scale=motion_scale,
**synthesis_kwargs)
return img_dict
from Next3d.training_avatar_texture.networks_stylegan2_styleunet_next3d import EncoderResBlock
class EncoderTriplane(torch.nn.Module):
def __init__(self):
super().__init__()
# encoder
self.encoder = torch.nn.ModuleList()
config_lists = [
[64, 128, 1, 1],
[128, 256, 2, 1],
[256, 512, 2, 2],
[512, 512, 2, 4],
[512, 32, 1, 8],
]
for config_list in config_lists:
block = EncoderResBlock(33, config_list[0], config_list[1], down=config_list[2], downsample=config_list[3])
self.encoder.append(block)
def forward(self, init_input):
# obtain multi-scale content features
cond_list = []
cond_out = None
x_in = init_input
for i, _ in enumerate(self.encoder):
x_in, cond_out = self.encoder[i](x_in, cond_out)
cond_list.append(cond_out)
cond_list = cond_list[::-1]
return cond_list |