Frame-Level Smooth Motion Learning for Human Mesh Recovery

Reconstructing accurate and smooth 3D human mesh from a monocular video is still a challenge, due to the temporal consistency requirement of body movements. A frame-level smooth motion learning approach called SLMR model was proposed in this work. Specifically, we first design the temporal encoding with a multi-headed attention mechanism, which captures the global and local temporal context relations of motion to retain more dynamic motion features for improving mesh recovery accuracy. We also propose a probabilistic generative model consisting of a conditional variational autoencoder, which learns the distribution of pose changes in each frame, and solves temporal inconsistency of the body movement. Compared with existing approaches, SLMR can take full advantage of inter-frame motion contexts. Experiments validated the effectiveness and smoothness of the proposed approach for human mesh recovery in the wild.