Neural Marionette: Unsupervised Learning of Motion Skeleton and Latent Dynamics from Volumetric Video
This addresses the challenge of generating diverse motions without prior knowledge for applications in animation and robotics, representing an incremental advance in unsupervised motion learning.
The paper tackles the problem of unsupervised discovery of skeletal structure and motion dynamics from volumetric video, achieving results comparable to hand-labeled ground truth skeletons.
We present Neural Marionette, an unsupervised approach that discovers the skeletal structure from a dynamic sequence and learns to generate diverse motions that are consistent with the observed motion dynamics. Given a video stream of point cloud observation of an articulated body under arbitrary motion, our approach discovers the unknown low-dimensional skeletal relationship that can effectively represent the movement. Then the discovered structure is utilized to encode the motion priors of dynamic sequences in a latent structure, which can be decoded to the relative joint rotations to represent the full skeletal motion. Our approach works without any prior knowledge of the underlying motion or skeletal structure, and we demonstrate that the discovered structure is even comparable to the hand-labeled ground truth skeleton in representing a 4D sequence of motion. The skeletal structure embeds the general semantics of possible motion space that can generate motions for diverse scenarios. We verify that the learned motion prior is generalizable to the multi-modal sequence generation, interpolation of two poses, and motion retargeting to a different skeletal structure.