  We introduce HuMoR: a 3D Human Motion Model for Robust Estimation of temporal
pose and shape. Though substantial progress has been made in estimating 3D
human motion and shape from dynamic observations, recovering plausible pose
sequences in the presence of noise and occlusions remains a challenge. For this
purpose, we propose an expressive generative model in the form of a conditional
variational autoencoder, which learns a distribution of the change in pose at
each step of a motion sequence. Furthermore, we introduce a flexible
optimization-based approach that leverages HuMoR as a motion prior to robustly
estimate plausible pose and shape from ambiguous observations. Through
extensive evaluations, we demonstrate that our model generalizes to diverse
motions and body shapes after training on a large motion capture dataset, and
enables motion reconstruction from multiple input modalities including 3D
keypoints and RGB(-D) videos.
