LEAP (LEarning Articulated occupancy of People), a novel neural occupancy representation of the human body. It is effectively an implitic version of SMPL. 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.

LEAP (LEarning Articulated occupancy of People), a novel neural occupancy representation of the human body. It is effectively an implitic version of SMPL. 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.