  Substantial progress has been made on modeling rigid 3D objects using deep
implicit representations. Yet, extending these methods to learn neural models
of human shape is still in its infancy. Human bodies are complex and the key
challenge is to learn a representation that generalizes such that it can
express body shape deformations for unseen subjects in unseen,
highly-articulated, poses. To address this challenge, we introduce LEAP
(LEarning Articulated occupancy of People), a novel neural occupancy
representation of the human body. Given a set of bone transformations (i.e.
joint locations and rotations) and a query point in space, LEAP first maps the
query point to a canonical space via learned linear blend skinning (LBS)
functions and then efficiently queries the occupancy value via an occupancy
network that models accurate identity- and pose-dependent deformations in the
canonical space. Experiments show that our canonicalized occupancy estimation
with the learned LBS functions greatly improves the generalization capability
of the learned occupancy representation across various human shapes and poses,
outperforming existing solutions in all settings.
