  Efficient representation of articulated objects such as human bodies is an
important problem in computer vision and graphics. To efficiently simulate
deformation, existing approaches represent 3D objects using polygonal meshes
and deform them using skinning techniques. This paper introduces neural
articulated shape approximation (NASA), an alternative framework that enables
efficient representation of articulated deformable objects using neural
indicator functions that are conditioned on pose. Occupancy testing using NASA
is straightforward, circumventing the complexity of meshes and the issue of
water-tightness. We demonstrate the effectiveness of NASA for 3D tracking
applications, and discuss other potential extensions.
