  Human pose transfer, as a misaligned image generation task, is very
challenging. Existing methods cannot effectively utilize the input information,
which often fail to preserve the style and shape of hair and clothes. In this
paper, we propose an adaptive human pose transfer network with two hierarchical
deformation levels. The first level generates human semantic parsing aligned
with the target pose, and the second level generates the final textured person
image in the target pose with the semantic guidance. To avoid the drawback of
vanilla convolution that treats all the pixels as valid information, we use
gated convolution in both two levels to dynamically select the important
features and adaptively deform the image layer by layer. Our model has very few
parameters and is fast to converge. Experimental results demonstrate that our
model achieves better performance with more consistent hair, face and clothes
with fewer parameters than state-of-the-art methods. Furthermore, our method
can be applied to clothing texture transfer.
