  Human bodies exhibit various shapes for different identities or poses, but
the body shape has certain similarities in structure and thus can be embedded
in a low-dimensional space. This paper presents an autoencoder-like network
architecture to learn disentangled shape and pose embedding specifically for
the 3D human body. This is inspired by recent progress of deformation-based
latent representation learning. To improve the reconstruction accuracy, we
propose a hierarchical reconstruction pipeline for the disentangling process
and construct a large dataset of human body models with consistent connectivity
for the learning of the neural network. Our learned embedding can not only
achieve superior reconstruction accuracy but also provide great flexibility in
3D human body generation via interpolation, bilinear interpolation, and latent
space sampling. The results from extensive experiments demonstrate the
powerfulness of our learned 3D human body embedding in various applications.

Computer Vision and Pattern Recognition;Graphics;Machine Learning