| | |
| |
|
| | """ |
| | Stitching module(S) and two retargeting modules(R) defined in the paper. |
| | |
| | - The stitching module pastes the animated portrait back into the original image space without pixel misalignment, such as in |
| | the stitching region. |
| | |
| | - The eyes retargeting module is designed to address the issue of incomplete eye closure during cross-id reenactment, especially |
| | when a person with small eyes drives a person with larger eyes. |
| | |
| | - The lip retargeting module is designed similarly to the eye retargeting module, and can also normalize the input by ensuring that |
| | the lips are in a closed state, which facilitates better animation driving. |
| | """ |
| | from torch import nn |
| |
|
| |
|
| | class StitchingRetargetingNetwork(nn.Module): |
| | def __init__(self, input_size, hidden_sizes, output_size): |
| | super(StitchingRetargetingNetwork, self).__init__() |
| | layers = [] |
| | for i in range(len(hidden_sizes)): |
| | if i == 0: |
| | layers.append(nn.Linear(input_size, hidden_sizes[i])) |
| | else: |
| | layers.append(nn.Linear(hidden_sizes[i - 1], hidden_sizes[i])) |
| | layers.append(nn.ReLU(inplace=True)) |
| | layers.append(nn.Linear(hidden_sizes[-1], output_size)) |
| | self.mlp = nn.Sequential(*layers) |
| |
|
| | def initialize_weights_to_zero(self): |
| | for m in self.modules(): |
| | if isinstance(m, nn.Linear): |
| | nn.init.zeros_(m.weight) |
| | nn.init.zeros_(m.bias) |
| |
|
| | def forward(self, x): |
| | return self.mlp(x) |
| |
|
