  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.
