Using Claude Opus to reverse engineer code from white paper (this is for La Raza)
modules cherry picked from https://github.com/yerfor/Real3DPortrait/
fyi - keep eyes on this repo - Egor Zakharov who worked on MegaPortraits as lead AI worked on this https://github.com/MBZUAI-Metaverse/VOODOO3D-official
All the models / code created in Net.py from Claude Opus. The Real3DPortrait code seems to provide warping / volumetric models as a downstream implmentation of MegaPortrait foundation code.
class Canonical3DVolumeEncoder(nn.Module):
def __init__(self, model_scale='standard'):
super().__init__()
use_weight_norm = False
down_seq = [3, 64, 128, 256, 512]
n_res = 2
self.in_conv = ConvBlock2D("CNA", 3, down_seq[0], 7, 1, 3, use_weight_norm)
self.down = nn.Sequential(*[DownBlock2D(down_seq[i], down_seq[i + 1], use_weight_norm) for i in range(len(down_seq) - 1)])
self.res = nn.Sequential(*[ResBlock2D(down_seq[-1], use_weight_norm) for _ in range(n_res)])
self.out_conv = nn.Conv2d(down_seq[-1], 64, 1, 1, 0)
def forward(self, x):
x = self.in_conv(x)
x = self.down(x)
x = self.res(x)
x = self.out_conv(x)
return x
class IdentityEncoder(nn.Module):
def __init__(self, model_scale='standard'):
super().__init__()
use_weight_norm = False
down_seq = [3, 64, 128, 256, 512]
n_res = 2
self.in_conv = ConvBlock2D("CNA", 3, down_seq[0], 7, 1, 3, use_weight_norm)
self.down = nn.Sequential(*[DownBlock2D(down_seq[i], down_seq[i + 1], use_weight_norm) for i in range(len(down_seq) - 1)])
self.res = nn.Sequential(*[ResBlock2D(down_seq[-1], use_weight_norm) for _ in range(n_res)])
self.out_conv = nn.Conv2d(down_seq[-1], 512, 1, 1, 0)
def forward(self, x):
x = self.in_conv(x)
x = self.down(x)
x = self.res(x)
x = self.out_conv(x)
x = x.view(x.shape[0], -1)
return x
class HeadPoseEncoder(nn.Module):
def __init__(self, model_scale='standard'):
super().__init__()
use_weight_norm = False
down_seq = [3, 64, 128, 256]
n_res = 2
self.in_conv = ConvBlock2D("CNA", 3, down_seq[0], 7, 1, 3, use_weight_norm)
self.down = nn.Sequential(*[DownBlock2D(down_seq[i], down_seq[i + 1], use_weight_norm) for i in range(len(down_seq) - 1)])
self.res = nn.Sequential(*[ResBlock2D(down_seq[-1], use_weight_norm) for _ in range(n_res)])
self.out_conv = nn.Conv2d(down_seq[-1], 3, 1, 1, 0)
def forward(self, x):
x = self.in_conv(x)
x = self.down(x)
x = self.res(x)
x = self.out_conv(x)
x = x.view(x.shape[0], -1)
return x
class FacialDynamicsEncoder(nn.Module):
def __init__(self, model_scale='standard'):
super().__init__()
use_weight_norm = False
down_seq = [3, 64, 128, 256, 512]
n_res = 2
self.in_conv = ConvBlock2D("CNA", 3, down_seq[0], 7, 1, 3, use_weight_norm)
self.down = nn.Sequential(*[DownBlock2D(down_seq[i], down_seq[i + 1], use_weight_norm) for i in range(len(down_seq) - 1)])
self.res = nn.Sequential(*[ResBlock2D(down_seq[-1], use_weight_norm) for _ in range(n_res)])
self.out_conv = nn.Conv2d(down_seq[-1], 256, 1, 1, 0)
def forward(self, x):
x = self.in_conv(x)
x = self.down(x)
x = self.res(x)
x = self.out_conv(x)
x = x.view(x.shape[0], -1)
return x
class ExpressiveDisentangledFaceLatentSpace(nn.Module):
def __init__(self):
super().__init__()
# Encoders
self.canonical_3d_volume_encoder = Canonical3DVolumeEncoder()
self.identity_encoder = IdentityEncoder()
self.head_pose_encoder = HeadPoseEncoder()
self.facial_dynamics_encoder = FacialDynamicsEncoder()
# Decoder
self.decoder = Decoder()
self.fh = FaceHelper()
# Loss functions
self.reconstruction_loss = nn.L1Loss()
self.pairwise_transfer_loss = nn.L1Loss()
self.identity_similarity_loss = nn.CosineSimilarity()
def forward(self, img1, img2):
# Extract latent variables for img1
V_a1 = self.canonical_3d_volume_encoder(img1)
z_id1 = self.identity_encoder(img1)
z_pose1 = self.head_pose_encoder(img1)
z_dyn1 = self.facial_dynamics_encoder(img1)
# Extract latent variables for img2
V_a2 = self.canonical_3d_volume_encoder(img2)
z_id2 = self.identity_encoder(img2)
z_pose2 = self.head_pose_encoder(img2)
z_dyn2 = self.facial_dynamics_encoder(img2)
# Reconstruct images
img1_recon = self.decoder(V_a1, z_id1, z_pose1, z_dyn1)
img2_recon = self.decoder(V_a2, z_id2, z_pose2, z_dyn2)
# Pairwise head pose and facial dynamics transfer
img1_pose_transfer = self.decoder(V_a1, z_id1, z_pose2, z_dyn1)
img2_dyn_transfer = self.decoder(V_a2, z_id2, z_pose2, z_dyn1)
# Cross-identity pose and facial motion transfer
img1_cross_id_transfer = self.decoder(V_a1, z_id2, z_pose1, z_dyn1)
img2_cross_id_transfer = self.decoder(V_a2, z_id1, z_pose2, z_dyn2)
return img1_recon, img2_recon, img1_pose_transfer, img2_dyn_transfer, img1_cross_id_transfer, img2_cross_id_transfer
def training_step(self, img1, img2):
# Forward pass
img1_recon, img2_recon, img1_pose_transfer, img2_dyn_transfer, img1_cross_id_transfer, img2_cross_id_transfer = self.forward(img1, img2)
# Reconstruction loss
loss_recon = self.reconstruction_loss(img1_recon, img1) + self.reconstruction_loss(img2_recon, img2)
# Pairwise transfer loss
loss_pairwise_transfer = self.pairwise_transfer_loss(img1_pose_transfer, img2_dyn_transfer)
# Identity similarity loss
id_feat1 = self.fh.extract_identity_features(img1)
id_feat1_cross_id_transfer = self.fh.extract_identity_features(img1_cross_id_transfer)
id_feat2 = self.fh.extract_identity_features(img2)
id_feat2_cross_id_transfer = self.fh.extract_identity_features(img2_cross_id_transfer)
loss_id_sim = 1 - self.identity_similarity_loss(id_feat1, id_feat1_cross_id_transfer) + 1 - self.identity_similarity_loss(id_feat2, id_feat2_cross_id_transfer)
# Total loss
total_loss = loss_recon + loss_pairwise_transfer + loss_id_sim
return total_loss
class FaceEncoder(nn.Module):
def __init__(self, use_weight_norm=False):
super(FaceEncoder, self).__init__()
# Define encoder architecture for extracting latent variables
# - Canonical 3D appearance volume (V_can)
# - Identity code (z_id)
# - 3D head pose (z_pose)
# - Facial dynamics code (z_dyn)
# ...
self.in_conv = ConvBlock2D("CNA", 3, 64, 7, 1, 3, use_weight_norm)
self.down = nn.Sequential(
DownBlock2D(64, 128, use_weight_norm),
DownBlock2D(128, 256, use_weight_norm),
DownBlock2D(256, 512, use_weight_norm)
)
self.mid_conv = nn.Conv2d(512, 32 * 16, 1, 1, 0)
self.res = nn.Sequential(
ResBlock3D(32, use_weight_norm),
ResBlock3D(32, use_weight_norm),
ResBlock3D(32, use_weight_norm),
ResBlock3D(32, use_weight_norm),
ResBlock3D(32, use_weight_norm),
ResBlock3D(32, use_weight_norm)
)
self.identity_encoder = nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Linear(256, 128)
)
self.head_pose_encoder = nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Linear(256, 6)
)
self.facial_dynamics_encoder = nn.Sequential(
nn.Linear(512, 256),
nn.ReLU(inplace=True),
nn.Linear(256, 64)
)
def forward(self, x):
x = self.in_conv(x)
x = self.down(x)
x = self.mid_conv(x)
N, _, H, W = x.shape
x = x.view(N, 32, 16, H, W)
appearance_volume = self.res(x)
# Extract identity code, head pose, and facial dynamics code
x = x.view(N, -1)
identity_code = self.identity_encoder(x)
head_pose = self.head_pose_encoder(x)
facial_dynamics = self.facial_dynamics_encoder(x)
return appearance_volume, identity_code, head_pose, facial_dynamics
class FaceDecoder(nn.Module):
def __init__(self, use_weight_norm=True):
super(FaceDecoder, self).__init__()
self.in_conv = ConvBlock2D("CNA", 32 * 16, 256, 3, 1, 1, use_weight_norm, nonlinearity_type="leakyrelu")
self.res = nn.Sequential(
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm)
)
self.up = nn.Sequential(
nn.Upsample(scale_factor=2),
ConvBlock2D("CNA", 256, 128, 3, 1, 1, use_weight_norm),
nn.Upsample(scale_factor=2),
ConvBlock2D("CNA", 128, 64, 3, 1, 1, use_weight_norm),
nn.Upsample(scale_factor=2),
ConvBlock2D("CNA", 64, 3, 7, 1, 3, use_weight_norm, activation_type="tanh")
)
def forward(self, appearance_volume, identity_code, head_pose, facial_dynamics):
N, _, D, H, W = appearance_volume.shape
x = appearance_volume.view(N, -1, H, W)
x = self.in_conv(x)
x = self.res(x)
# Apply 3D warping based on head pose and facial dynamics
# ...
face_image = self.up(x)
return face_image
# Define the diffusion transformer for holistic facial dynamics generation
'''
In the provided code snippet for the DiffusionTransformer class, the transformer architecture is implemented using the nn.TransformerEncoderLayer module from PyTorch. The queries, keys, and values are internally computed within each transformer layer based on the input features.
The nn.TransformerEncoderLayer module takes care of computing the queries, keys, and values from the input features using linear transformations. The attention mechanism in the transformer layer then uses these queries, keys, and values to compute the self-attention weights and update the input features.
Here's a breakdown of the transformer architecture in the code:
The DiffusionTransformer class is initialized with the number of layers (num_layers), number of attention heads (num_heads), hidden size (hidden_size), and dropout probability (dropout).
In the __init__ method, the class creates a nn.ModuleList called self.layers, which contains num_layers instances of nn.TransformerEncoderLayer. Each transformer layer has the specified hidden_size, num_heads, and dropout probability.
The forward method takes the input features x, audio_features, gaze_direction, head_distance, and emotion_offset.
The input features are concatenated along the last dimension using torch.cat to form a single tensor input_features.
The concatenated input_features tensor is then passed through each transformer layer in self.layers using a loop. Inside each transformer layer, the following operations are performed:
The input features are linearly transformed to compute the queries, keys, and values.
The attention mechanism computes the self-attention weights using the queries, keys, and values.
The self-attention weights are used to update the input features.
The updated features are passed through a feedforward neural network.
Residual connections and layer normalization are applied.
After passing through all the transformer layers, the output features are normalized using nn.LayerNorm in self.norm(x).
The final output x is returned, which represents the processed features after applying the transformer layers.
The transformer architecture in this code leverages the self-attention mechanism to capture dependencies and relationships among the input features. The queries, keys, and values are internally computed within each transformer layer based on the input features, allowing the model to learn and update the feature representations through the attention mechanism.
'''
class DiffusionTransformer(nn.Module):
def __init__(self, num_layers, num_heads, hidden_size, dropout=0.1):
super(DiffusionTransformer, self).__init__()
self.layers = nn.ModuleList([
nn.TransformerEncoderLayer(hidden_size, num_heads, hidden_size, dropout)
for _ in range(num_layers)
])
self.norm = nn.LayerNorm(hidden_size)
def forward(self, x, audio_features, gaze_direction, head_distance, emotion_offset):
# Concatenate input features
input_features = torch.cat([x, audio_features, gaze_direction, head_distance, emotion_offset], dim=-1)
# Apply transformer layers
for layer in self.layers:
x = layer(input_features)
x = self.norm(x)
return x
# Decoder
'''
we extract the head pose parameters (yaw, pitch, roll, and translation t) and facial dynamics (delta) from the input latent codes z_pose and z_dyn.
We then use the transform_kp function to transform the keypoints based on the head pose and facial dynamics. This function applies the necessary transformations to the canonical 3D volume V_can to obtain the transformed keypoints kp_pose.
Next, we create a 2D coordinate grid using make_coordinate_grid_2d and repeat it for each batch sample. We add the transformed keypoints kp_pose to the grid to obtain the transformed grid coordinates.
Finally, we use F.grid_sample to warp the feature volume x using the transformed grid coordinates. The warped feature volume x_warped is then passed through the upsampling layers to generate the final face image.
Note that you may need to adjust the dimensions and shapes of the tensors based on your specific implementation and the dimensions of V_can and the latent codes.
'''
class Decoder(nn.Module):
def __init__(self, use_weight_norm=True):
super(Decoder, self).__init__()
self.in_conv = ConvBlock2D("CNA", 64, 256, 3, 1, 1, use_weight_norm, nonlinearity_type="leakyrelu")
self.res = nn.Sequential(
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm),
ResBlock2D(256, use_weight_norm)
)
self.up = nn.Sequential(
nn.Upsample(scale_factor=2),
ConvBlock2D("CNA", 256, 128, 3, 1, 1, use_weight_norm),
nn.Upsample(scale_factor=2),
ConvBlock2D("CNA", 128, 64, 3, 1, 1, use_weight_norm),
nn.Upsample(scale_factor=2),
ConvBlock2D("CNA", 64, 3, 7, 1, 3, use_weight_norm, activation_type="tanh")
)
self.fh = FaceHelper()
def forward(self, V_can, z_id, z_pose, z_dyn):
N, C, D, H, W = V_can.shape
x = V_can.view(N, -1, H, W)
x = self.in_conv(x)
x = self.res(x)
# Apply 3D warping based on head pose and facial dynamics
# yaw, pitch, roll = z_pose[:, 0], z_pose[:, 1], z_pose[:, 2]
yaw, pitch, roll = self.fh.calculate_pose(z_pose)
t = z_pose[:, 3:]
delta = z_dyn
# Transform keypoints based on head pose and facial dynamics
kp_pose, R = transform_kp(V_can, yaw, pitch, roll, t, delta)
# Warp the feature volume using the transformed keypoints
grid = make_coordinate_grid_2d(x.shape[2:]).unsqueeze(0).repeat(N, 1, 1, 1).to(x.device)
grid = grid.view(N, -1, 2)
kp_pose = kp_pose.view(N, -1, 2)
grid_transformed = grid + kp_pose
grid_transformed = grid_transformed.view(N, x.shape[2], x.shape[3], 2)
x_warped = F.grid_sample(x, grid_transformed, align_corners=True)
face_image = self.up(x_warped)
return face_imageHere's how the provided code aligns with the VASA paper:
1. Face Latent Space Construction:
- The code defines encoders for extracting various latent variables from face images, similar to the approach mentioned in the VASA paper.
- The `Canonical3DVolumeEncoder`, `IdentityEncoder`, `HeadPoseEncoder`, and `FacialDynamicsEncoder` classes correspond to the encoders for extracting the canonical 3D appearance volume, identity code, 3D head pose, and facial dynamics code, respectively.
- The `ExpressiveDisentangledFaceLatentSpace` class combines these encoders and a decoder to form the overall framework for learning the disentangled face latent space.
- The loss functions used in the `ExpressiveDisentangledFaceLatentSpace` class, such as reconstruction loss, pairwise transfer loss, and identity similarity loss, align with the losses mentioned in the paper for achieving disentanglement and expressiveness.
2. Holistic Facial Dynamics Generation with Diffusion Transformer:
- The `DiffusionTransformer` class in the code represents the diffusion transformer model used for generating holistic facial dynamics and head motion.
- The architecture of the `DiffusionTransformer` class, with its transformer layers and input feature concatenation, aligns with the description in the VASA paper.
- The forward method of the `DiffusionTransformer` class takes in the latent codes, audio features, gaze direction, head distance, and emotion offset as conditioning signals, similar to the approach mentioned in the paper.
3. Talking Face Video Generation:
- The `Decoder` class in the code corresponds to the decoder mentioned in the VASA paper for generating talking face videos.
- The decoder takes the canonical 3D appearance volume, identity code, head pose, and facial dynamics latent codes as input and applies 3D warping based on the head pose and facial dynamics to generate the final face image.
- The warping process in the decoder, using the `transform_kp` function and grid sampling, aligns with the description in the paper for applying the generated motion latent codes to the appearance volume.
Overall, the code follows the high-level architecture and components described in the VASA paper, including the face latent space construction, holistic facial dynamics generation with diffusion transformer, and the decoder for generating talking face videos. The specific implementation details and function names may differ, but the overall structure and flow of the code align with the concepts presented in the paper.REFERENCES - condition signals gaze direction Accurate 3d face reconstruction with weakly-supervised learning https://github.com/Microsoft/Deep3DFaceReconstruction
HSEmotion: High-speed emotion recognition library https://github.com/av-savchenko/face-emotion-recognition/tree/main
Arface - feature extractor https://github.com/deepinsight/insightface/tree/master/recognition/arcface_torch