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"""
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Stitching module(S) and two retargeting modules(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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