论文标题
收集拼图片：解开的自我驱动的人姿势转移通过置换纹理
Collecting The Puzzle Pieces: Disentangled Self-Driven Human Pose Transfer by Permuting Textures
论文作者
论文摘要
人姿势转移综合了一个人的新观点，以给定的姿势。最近的工作通过自我重建实现了这一点，该工作通过将人分为一部分，然后重新组合重建来解散一个人的姿势和纹理信息。但是，零件级别的分离保留了一些姿势信息，这些信息可能会产生不必要的人工制品。在本文中，我们提出了通过置换纹理（PT $^2 $）的姿势转移，这是一种自动驱动人类姿势转移的方法，该方法将构成姿势置于斑点级别的纹理中。具体而言，我们通过置换图像补丁来从输入图像中删除姿势，因此仅保留纹理信息。然后，我们通过从排列的纹理中采样以进行补丁级的分离来重建输入图像。为了减少噪声并从排列的补丁中恢复服装形状信息，我们在三重分支网络中使用具有多个内核大小的编码器。在DeepFashion和Market-1501上，PT $^2 $报告了自动指标比其他自我驱动方法的显着增长，甚至超过了一些完全监督的方法。一项用户研究还报告了我们方法生成的图像在68％的案例中比先前工作的自我驱动方法更受欢迎。代码可从https://github.com/nannanli999/pt_square获得。
Human pose transfer synthesizes new view(s) of a person for a given pose. Recent work achieves this via self-reconstruction, which disentangles a person's pose and texture information by breaking the person down into parts, then recombines them for reconstruction. However, part-level disentanglement preserves some pose information that can create unwanted artifacts. In this paper, we propose Pose Transfer by Permuting Textures (PT$^2$), an approach for self-driven human pose transfer that disentangles pose from texture at the patch-level. Specifically, we remove pose from an input image by permuting image patches so only texture information remains. Then we reconstruct the input image by sampling from the permuted textures for patch-level disentanglement. To reduce noise and recover clothing shape information from the permuted patches, we employ encoders with multiple kernel sizes in a triple branch network. On DeepFashion and Market-1501, PT$^2$ reports significant gains on automatic metrics over other self-driven methods, and even outperforms some fully-supervised methods. A user study also reports images generated by our method are preferred in 68% of cases over self-driven approaches from prior work. Code is available at https://github.com/NannanLi999/pt_square.
