  In this paper, we focus on person image generation, namely, generating person
image under various conditions, e.g., corrupted texture or different pose. To
address texture occlusion and large pose misalignment in this task, previous
works just use the corresponding region's style to infer the occluded area and
rely on point-wise alignment to reorganize the context texture information,
lacking the ability to globally correlate the region-wise style codes and
preserve the local structure of the source. To tackle these problems, we
present a GLocal framework to improve the occlusion-aware texture estimation by
globally reasoning the style inter-correlations among different semantic
regions, which can also be employed to recover the corrupted images in texture
inpainting. For local structural information preservation, we further extract
the local structure of the source image and regain it in the generated image
via local structure transfer. We benchmark our method to fully characterize its
performance on DeepFashion dataset and present extensive ablation studies that
highlight the novelty of our method.
