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Human pose transfer, as a misaligned image generation task, is verychallenging. Existing methods cannot effectively utilize the input information,which often fail to preserve the style and shape of hair and clothes. In thispaper, we propose an adaptive human pose transfer network with two hierarchicaldeformation levels. The first level generates human semantic parsing alignedwith the target pose, and the second level generates the final textured personimage in the target pose with the semantic guidance. To avoid the drawback ofvanilla convolution that treats all the pixels as valid information, we usegated convolution in both two levels to dynamically select the importantfeatures and adaptively deform the image layer by layer. Our model has very fewparameters and is fast to converge. Experimental results demonstrate that ourmodel achieves better performance with more consistent hair, face and clotheswith fewer parameters than state-of-the-art methods. Furthermore, our methodcan be applied to clothing texture transfer.

Human Pose Transfer by Adaptive Hierarchical Deformation