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| # coding: utf-8 | |
| """ | |
| 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) | |