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Weakly supervised 2D human pose transfer
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DOI：10.1007/s11432-021-3301-5.png)
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Abstract
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We present a novel method for pose transfer between two 2D human skeletons. When the bone lengths and proportions between the two skeletons are significantly different, pose transfer becomes a challenging task, which cannot be accomplished by simply copying the joint positions or the bone directions. Our data-driven approach utilizes a deep neural network trained, in a weakly supervised fashion, to encode a skeleton into two separate latent codes, one representing its pose, and another representing the skeleton's proportions (skeleton-ID). The network is given two skeletons, and learns to combine the pose of one with the skeleton-ID of the other. Lacking supervision on the poses, we develop a novel loss that qualitatively compares poses of different skeletons. We evaluate the performance of our method on a large set of poses. The advantages of avoiding supervision are demonstrated by showing transfer of extreme poses, as well as between uncommon skeleton proportions.
Keywords:
pose transfer
weak supervision
human skeleton
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