  We deal with the controllable person image synthesis task which aims to
re-render a human from a reference image with explicit control over body pose
and appearance. Observing that person images are highly structured, we propose
to generate desired images by extracting and distributing semantic entities of
reference images. To achieve this goal, a neural texture extraction and
distribution operation based on double attention is described. This operation
first extracts semantic neural textures from reference feature maps. Then, it
distributes the extracted neural textures according to the spatial
distributions learned from target poses. Our model is trained to predict human
images in arbitrary poses, which encourages it to extract disentangled and
expressive neural textures representing the appearance of different semantic
entities. The disentangled representation further enables explicit appearance
control. Neural textures of different reference images can be fused to control
the appearance of the interested areas. Experimental comparisons show the
superiority of the proposed model. Code is available at
https://github.com/RenYurui/Neural-Texture-Extraction-Distribution.
