Lie Algebra-Based Kinematic Prior for 3D Human Pose Tracking

Edgar Simo Serra, Carme Torras, Francesc Moreno-Noguer

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

We propose a novel kinematic prior for 3D human pose tracking that allows predicting the position in subsequent frames given the current position. We first define a Riemannian manifold that models the pose and extend it with its Lie algebra to also be able to represent the kinematics. We then learn a joint Gaussian mixture model of both the human pose and the kinematics on this manifold. Finally by conditioning the kinematics on the pose we are able to obtain a distribution of poses for subsequent frames that which can be used as a reliable prior in 3D human pose tracking. Our model scales well to large amounts of data and can be sampled at over 100,000 samples/second. We show it outperforms the widely used Gaussian diffusion model on the challenging Human3.6M dataset.

Original languageEnglish
Title of host publicationProceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages394-397
Number of pages4
ISBN (Electronic)9784901122153
DOIs
Publication statusPublished - 2015 Jul 8
Externally publishedYes
Event14th IAPR International Conference on Machine Vision Applications, MVA 2015 - Tokyo, Japan
Duration: 2015 May 182015 May 22

Other

Other14th IAPR International Conference on Machine Vision Applications, MVA 2015
CountryJapan
CityTokyo
Period15/5/1815/5/22

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ASJC Scopus subject areas

  • Computer Science Applications
  • Computer Vision and Pattern Recognition

Cite this

Simo Serra, E., Torras, C., & Moreno-Noguer, F. (2015). Lie Algebra-Based Kinematic Prior for 3D Human Pose Tracking. In Proceedings of the 14th IAPR International Conference on Machine Vision Applications, MVA 2015 (pp. 394-397). [7153212] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/MVA.2015.7153212