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Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction

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Semi-supervised Hierarchical Models for 3D Human Pose Reconstruction
Recent research in visual inference from monocular images has shown that discriminatively trained image-based predictors can provide fast, automatic qualitative 3D reconstructions of human body pose or scene structure in realworld environments. However, the stability of existing image representations tends to be perturbed by deformations and misalignments in the training set, which, in turn, degrade the quality of learning and generalization. In this paper we advocate the semi-supervised learning of hierarchical image descriptions in order to better tolerate variability at multiple levels of detail. We combine multilevel encodings with improved stability to geometric transformations, with metric learning and semi-supervised manifold regularization methods in order to further profile them for taskinvariance ? resistance to background clutter and within the same human pose class differences. We quantitatively analyze the effectiveness of both descriptors and learning methods and show th...
Atul Kanaujia, Cristian Sminchisescu, Dimitris N.
Added 12 Oct 2009
Updated 12 Oct 2009
Type Conference
Year 2007
Where CVPR
Authors Atul Kanaujia, Cristian Sminchisescu, Dimitris N. Metaxas
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