  Due to unreliable geometric matching and content misalignment, most
conventional pose transfer algorithms fail to generate fine-trained person
images. In this paper, we propose a novel framework Spatial Content Alignment
GAN (SCAGAN) which aims to enhance the content consistency of garment textures
and the details of human characteristics. We first alleviate the spatial
misalignment by transferring the edge content to the target pose in advance.
Secondly, we introduce a new Content-Style DeBlk which can progressively
synthesize photo-realistic person images based on the appearance features of
the source image, the target pose heatmap and the prior transferred content in
edge domain. We compare the proposed framework with several state-of-the-art
methods to show its superiority in quantitative and qualitative analysis.
Moreover, detailed ablation study results demonstrate the efficacy of our
contributions. Codes are publicly available at
github.com/rocketappslab/SCA-GAN.
