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NASA Neural Articulated Shape Approximation

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12352)

Abstract

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 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.

Keywords

3D deep learning Neural object representation Articulated objects Deformation Skinning Occupancy Neural implicit functions 

Supplementary material

Supplementary material 1 (mp4 25693 KB)

504444_1_En_36_MOESM2_ESM.pdf (722 kb)
Supplementary material 2 (pdf 721 KB)

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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Google ResearchMountain ViewUSA
  2. 2.MPI for Informatics, Saarland Informatics CampusSaarbrückenGermany
  3. 3.University of TorontoTorontoCanada

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