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Human motion retargeting aims to transfer the motion of one person in a"driving" video or set of images to another person. Existing efforts leverage along training video from each target person to train a subject-specific motiontransfer model. However, the scalability of such methods is limited, as eachmodel can only generate videos for the given target subject, and such trainingvideos are labor-intensive to acquire and process. Few-shot motion transfertechniques, which only require one or a few images from a target, have recentlydrawn considerable attention. Methods addressing this task generally use either2D or explicit 3D representations to transfer motion, and in doing so,sacrifice either accurate geometric modeling or the flexibility of anend-to-end learned representation. Inspired by the Transformable BottleneckNetwork, which renders novel views and manipulations of rigid objects, wepropose an approach based on an implicit volumetric representation of the imagecontent, which can then be spatially manipulated using volumetric flow fields.We address the challenging question of how to aggregate information acrossdifferent body poses, learning flow fields that allow for combining contentfrom the appropriate regions of input images of highly non-rigid human subjectsperforming complex motions into a single implicit volumetric representation.This allows us to learn our 3D representation solely from videos of movingpeople. Armed with both 3D object understanding and end-to-end learnedrendering, this categorically novel representation delivers state-of-the-artimage generation quality, as shown by our quantitative and qualitativeevaluations.

Flow Guided Transformable Bottleneck Networks for Motion Retargeting