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Carlyx avatar Carlyx commented on August 27, 2024

是的,这个文件是数据处理过程中生成的,处理过程用到了[video-preprocessing。可以重新写一下dataloader直接使用mp4/png格式的数据。

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xz0305 avatar xz0305 commented on August 27, 2024

好的谢谢,可以发一下你的这个txt文件吗,我想看一下他的格式

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Carlyx avatar Carlyx commented on August 27, 2024

你好,我目前已经没有相关的文件了,但应该类似于:

1.mp4
2.mp4
3.mp4

表示训练集有上述3个视频。

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xz0305 avatar xz0305 commented on August 27, 2024

好的 非常感谢

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xz0305 avatar xz0305 commented on August 27, 2024

`损失函数中,为什么两次用到了自身重建对,与论文中提到的并不一致

        rec_loss = self.criterion_vgg(fake_selfpose, img_target).mean()*2
        rec_loss += self.criterion_vgg(fake_selfexp, img_source).mean()*2

rec_loss += F.l1_loss(fake_selfpose, img_target) rec_loss += F.l1_loss(fake_selfexp, img_source)

而且我理解的是,fake_selfpose和fake_selfexp都应该与img_source计算损失,为什么会使用到img_target呢

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xz0305 avatar xz0305 commented on August 27, 2024

你好 可以回答一下吗

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Carlyx avatar Carlyx commented on August 27, 2024

你好,感谢指出,已更正。自重建loss的目标是针对两个生成器进行自生成约束,因此GT应该和输入保持一致。实验中我们给vgg较大的权重,l1较小的权重,并且发现权重的轻微变化对于结果的影响较小。

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