  Controllable person image generation aims to produce realistic human images
with desirable attributes such as a given pose, cloth textures, or hairstyles.
However, the large spatial misalignment between source and target images makes
the standard image-to-image translation architectures unsuitable for this task.
Most state-of-the-art methods focus on alignment for global pose-transfer
tasks. However, they fail to deal with region-specific texture-transfer tasks,
especially for person images with complex textures. To solve this problem, we
propose a novel Spatially-Adaptive Warped Normalization (SAWN) which integrates
a learned flow-field to warp modulation parameters. It allows us to efficiently
align person spatially-adaptive styles with pose features. Moreover, we propose
a novel Self-Training Part Replacement (STPR) strategy to refine the model for
the texture-transfer task, which improves the quality of the generated clothes
and the preservation ability of non-target regions. Our experimental results on
the widely used DeepFashion dataset demonstrate a significant improvement of
the proposed method over the state-of-the-art methods on pose-transfer and
texture-transfer tasks. The code is available at
https://github.com/zhangqianhui/Sawn.
