  Human pose transfer has received great attention due to its wide
applications, yet is still a challenging task that is not well solved. Recent
works have achieved great success to transfer the person image from the source
to the target pose. However, most of them cannot well capture the semantic
appearance, resulting in inconsistent and less realistic textures on the
reconstructed results. To address this issue, we propose a new two-stage
framework to handle the pose and appearance translation. In the first stage, we
predict the target semantic parsing maps to eliminate the difficulties of pose
transfer and further benefit the latter translation of per-region appearance
style. In the second one, with the predicted target semantic maps, we suggest a
new person image generation method by incorporating the region-adaptive
normalization, in which it takes the per-region styles to guide the target
appearance generation. Extensive experiments show that our proposed SPGNet can
generate more semantic, consistent, and photo-realistic results and perform
favorably against the state of the art methods in terms of quantitative and
qualitative evaluation. The source code and model are available at
https://github.com/cszy98/SPGNet.git.
