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We present Neural Marionette, an unsupervised approach that discovers theskeletal structure from a dynamic sequence and learns to generate diversemotions that are consistent with the observed motion dynamics. Given a videostream of point cloud observation of an articulated body under arbitrarymotion, our approach discovers the unknown low-dimensional skeletalrelationship that can effectively represent the movement. Then the discoveredstructure is utilized to encode the motion priors of dynamic sequences in alatent structure, which can be decoded to the relative joint rotations torepresent the full skeletal motion. Our approach works without any priorknowledge of the underlying motion or skeletal structure, and we demonstratethat the discovered structure is even comparable to the hand-labeled groundtruth skeleton in representing a 4D sequence of motion. The skeletal structureembeds the general semantics of possible motion space that can generate motionsfor diverse scenarios. We verify that the learned motion prior is generalizableto the multi-modal sequence generation, interpolation of two poses, and motionretargeting to a different skeletal structure.

Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video