NDLI: Observable subspaces for 3D human motion recovery

Content Provider	IEEE Xplore Digital Library
Author	Fossati, A. Salzmann, M. Fua, P.
Copyright Year	2009
Description	Author affiliation: CVLab, EPFL, China (Fossati, A.; Fua, P.) \|\| EECS, UC Berkeley, Berkeley, CA, USA (Salzmann, M.)
Abstract	The articulated body models used to represent human motion typically have many degrees of freedom, usually expressed as joint angles that are highly correlated. The true range of motion can therefore be represented by latent variables that span a low-dimensional space. This has often been used to make motion tracking easier. However, learning the latent space in a problem- independent way makes it non trivial to initialize the tracking process by picking appropriate initial values for the latent variables, and thus for the pose. In this paper, we show that by directly using observable quantities as our latent variables, we eliminate this problem and achieve full automation given only modest amounts of training data. More specifically, we exploit the fact that the trajectory of a person's feet or hands strongly constrains body pose in motions such as skating, skiing, or golfing. These trajectories are easy to compute and to parameterize using a few variables. We treat these as our latent variables and learn a mapping between them and sequences of body poses. In this manner, by simply tracking the feet or the hands, we can reliably guess initial poses over whole sequences and, then, refine them.
Starting Page	1137
Ending Page	1144
File Size	2550458
Page Count	8
File Format	PDF
ISBN	9781424439928
ISSN	10636919
DOI	10.1109/CVPR.2009.5206489
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-06-20
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Humans Trajectory Biological system modeling Automation Training data Joints Tracking Polynomials Gaussian processes Runtime
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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