BMVC09_H-GPLVM.pdf (1.05 MB)
Download file

Backing off: hierarchical decomposition of activity for 3D novel pose recovery

Download (1.05 MB)
conference contribution
posted on 10.02.2016, 11:44 by John Darby, Baihua LiBaihua Li, Nicholas Costen, David Fleet, Neil Lawrence
For model-based 3D human pose estimation, even simple models of the human body lead to high-dimensional state spaces. Where the class of activity is known a priori, low-dimensional activity models learned from training data make possible a thorough and efficient search for the best pose. Conversely, searching for solutions in the full state space places no restriction on the class of motion to be recovered, but is both difficult and expensive. This paper explores a potential middle ground between these approaches, using the hierarchical Gaussian process latent variable model to learn activity at different hierarchical scales within the human skeleton. We show that by training on full-body activity data then descending through the hierarchy in stages and exploring subtrees independently of one another, novel poses may be recovered. Experimental results on motion capture data and monocular video sequences demonstrate the utility of the approach, and comparisons are drawn with existing low-dimensional activity models. © 2009. The copyright of this document resides with its authors.

History

School

  • Science

Department

  • Computer Science

Published in

British Machine Vision Conference, BMVC 2009 - Proceedings

Citation

DARBY, J. ... et al, 2009. Backing off: hierarchical decomposition of activity for 3D novel pose recovery. British Machine Vision Conference, BMVC 2009 - Proceedings, 7th-10th September 2009, London, pp.11.1-11.11

Publisher

BMVC 2009 (© the authors)

Version

VoR (Version of Record)

Publisher statement

This work is made available according to the conditions of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) licence. Full details of this licence are available at: https://creativecommons.org/licenses/by-nc-nd/4.0/

Publication date

2009

Notes

The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

ISBN

1901725391;9781901725391

Language

en

Usage metrics

Keywords

Exports