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

NiLBS: Neural Inverse Linear Blend Skinning