  Person image synthesis, e.g., pose transfer, is a challenging problem due to
large variation and occlusion. Existing methods have difficulties predicting
reasonable invisible regions and fail to decouple the shape and style of
clothing, which limits their applications on person image editing. In this
paper, we propose PISE, a novel two-stage generative model for Person Image
Synthesis and Editing, which is able to generate realistic person images with
desired poses, textures, or semantic layouts. For human pose transfer, we first
synthesize a human parsing map aligned with the target pose to represent the
shape of clothing by a parsing generator, and then generate the final image by
an image generator. To decouple the shape and style of clothing, we propose
joint global and local per-region encoding and normalization to predict the
reasonable style of clothing for invisible regions. We also propose
spatial-aware normalization to retain the spatial context relationship in the
source image. The results of qualitative and quantitative experiments
demonstrate the superiority of our model on human pose transfer. Besides, the
results of texture transfer and region editing show that our model can be
applied to person image editing.
