  In this technical report, we investigate efficient representations of
articulated objects (e.g. human bodies), which is an important problem in
computer vision and graphics. To deform articulated geometry, existing
approaches represent objects as meshes and deform them using "skinning"
techniques. The skinning operation allows a wide range of deformations to be
achieved with a small number of control parameters. This paper introduces a
method to invert the deformations undergone via traditional skinning techniques
via a neural network parameterized by pose. The ability to invert these
deformations allows values (e.g., distance function, signed distance function,
occupancy) to be pre-computed at rest pose, and then efficiently queried when
the character is deformed. We leave empirical evaluation of our approach to
future work.
