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