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Project of "Adaptive Affine Transformation: A Simple and Effective Operation for Spatial Misaligned Image Generation"
个人觉得AdaAT算是FOMM类方法中出现的比较有意思的尝试,但我有个疑问想请教一下,我看作者构建的模型中,Appearance Encoder的输入中直接带有驱动目标的关键点信息,一般来说这样编解码,即使没有AdaAT的对Appearance Feature的deformation,应该也是能生成出对应的驱动结果的,这样就比较难说明实际生成结果主要是由AdaAT对Appearance Feature作用后生成的结果,还是利用Appearance Encoder中的驱动目标信息来生成的结果,不知道这样理解对不对?
full_grid = torch.cat([trans_grid, grid_z.unsqueeze(-1)], -1)
5D情况下grid.shape=(b,d,h,w,3),那么单独提取出grid[i,j,k,l]是否等于(z,y,x)?感觉很奇怪的定义。
When inferencing, it is the key point loaded from the txt file(example_person_target_kp.txt). Did you find the code that generates the key point?
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