论文标题
可控人图像综合的基于注意力的样式分布
Cross Attention Based Style Distribution for Controllable Person Image Synthesis
论文作者
论文摘要
可控的人图像综合任务可以通过对身体姿势和外观的明确控制来实现广泛的应用。在本文中，我们提出了一个基于跨注意的样式分布模块，该模块在源语义样式和目标姿势转移的目标姿势之间计算。该模块有意选择每个语义表示的样式，并根据目标姿势分配它们。跨注意的注意力矩阵表达了所有语义的目标姿势与源样式之间的动态相似性。因此，可以利用它将颜色和纹理从源图像路由，并受到目标解析映射的进一步限制，以实现更清晰的目标。同时，为了准确编码源外观，还添加了不同语义样式之间的自我关注。我们的模型的有效性在姿势转移和虚拟的尝试任务上进行了定量和定性验证。
Controllable person image synthesis task enables a wide range of applications through explicit control over body pose and appearance. In this paper, we propose a cross attention based style distribution module that computes between the source semantic styles and target pose for pose transfer. The module intentionally selects the style represented by each semantic and distributes them according to the target pose. The attention matrix in cross attention expresses the dynamic similarities between the target pose and the source styles for all semantics. Therefore, it can be utilized to route the color and texture from the source image, and is further constrained by the target parsing map to achieve a clearer objective. At the same time, to encode the source appearance accurately, the self attention among different semantic styles is also added. The effectiveness of our model is validated quantitatively and qualitatively on pose transfer and virtual try-on tasks.
