  A deep generative model that describes human motions can benefit a wide range
of fundamental computer vision and graphics tasks, such as providing robustness
to video-based human pose estimation, predicting complete body movements for
motion capture systems during occlusions, and assisting key frame animation
with plausible movements. In this paper, we present a method for learning
complex human motions independent of specific tasks using a combined global and
local latent space to facilitate coarse and fine-grained modeling.
Specifically, we propose a hierarchical motion variational autoencoder (HM-VAE)
that consists of a 2-level hierarchical latent space. While the global latent
space captures the overall global body motion, the local latent space enables
to capture the refined poses of the different body parts. We demonstrate the
effectiveness of our hierarchical motion variational autoencoder in a variety
of tasks including video-based human pose estimation, motion completion from
partial observations, and motion synthesis from sparse key-frames. Even though,
our model has not been trained for any of these tasks specifically, it provides
superior performance than task-specific alternatives. Our general-purpose human
motion prior model can fix corrupted human body animations and generate
complete movements from incomplete observations.
