3D Human Pose from Silhouettes by Relevance Vector Regression

Ankur Agarwal 1 Bill Triggs 1
1 LEAR - Learning and recognition in vision
GRAVIR - IMAG - Graphisme, Vision et Robotique, Inria Grenoble - Rhône-Alpes, CNRS - Centre National de la Recherche Scientifique : FR71
Abstract : 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 pans 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.
Type de document :
Communication dans un congrès
International Conference on Computer Vision & Pattern Recognition (CVPR '04), Jun 2004, Washington, United States. IEEE Computer Society, II, pp.882--888, 2004, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1315258〉. 〈10.1109/CVPR.2004.1315258〉
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Soumis le : lundi 20 décembre 2010 - 09:09:42
Dernière modification le : jeudi 11 janvier 2018 - 06:20:04
Document(s) archivé(s) le : lundi 21 mars 2011 - 03:16:14

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Ankur Agarwal, Bill Triggs. 3D Human Pose from Silhouettes by Relevance Vector Regression. International Conference on Computer Vision & Pattern Recognition (CVPR '04), Jun 2004, Washington, United States. IEEE Computer Society, II, pp.882--888, 2004, 〈http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=1315258〉. 〈10.1109/CVPR.2004.1315258〉. 〈inria-00548551〉

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