  Pose-guided person image synthesis aims to synthesize person images by
transforming reference images into target poses. In this paper, we observe that
the commonly used spatial transformation blocks have complementary advantages.
We propose a novel model by combining the attention operation with the
flow-based operation. Our model not only takes the advantage of the attention
operation to generate accurate target structures but also uses the flow-based
operation to sample realistic source textures. Both objective and subjective
experiments demonstrate the superiority of our model. Meanwhile, comprehensive
ablation studies verify our hypotheses and show the efficacy of the proposed
modules. Besides, additional experiments on the portrait image editing task
demonstrate the versatility of the proposed combination.
