PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

3D Human Pose from Silhouettes by Relevance Vector Regression
Ankur Agarwal and William Triggs
In: CVPR 2004, 29 Jun - 01 Jul 2004, Washington DC, USA.


We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor prior labelling of body parts in the image. Instead, it recovers pose by direct nonlinear regression against shape descriptor vectors extracted automatically from image silhouettes. For robustness against local silhouette segmentation errors, silhouette shape is encoded by histogram-of-shape-contexts descriptors. For the main regression, we evaluate both regularized least squares and Relevance Vector Machine (RVM) regressors over both linear and kernel bases. The RVM's provide much sparser regressors without compromising performance, and kernel bases give a small but worthwhile improvement in performance. For realism and good generalization with respect to viewpoints, we train the regressors on images resynthesized from real human motion capture data, and test it both quantitatively on similar independent test data, and qualitatively on a real image sequence. Mean angular errors of 6--7 degrees are obtained --- a factor of 3 better than the current state of the art for the much simpler upper body problem.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Machine Vision
Learning/Statistics & Optimisation
ID Code:195
Deposited By:William Triggs
Deposited On:05 June 2004