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Estimating 3D Human Pose from Single Images Using Iterative Refinement of the Prior

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2 Author(s)
Daubney, B. ; Dept. of Comput. Sci., Swansea Univ., Swansea, UK ; Xianghua Xie

This paper proposes a generative method to extract 3D human pose using just a single image. Unlike many existing approaches we assume that accurate foreground background segmentation is not possible and do not use binary silhouettes. A stochastic method is used to search the pose space and the posterior distribution is maximized using Expectation Maximization (EM). It is assumed that some knowledge is known a priori about the position, scale and orientation of the person present and we specifically develop an approach to exploit this. The result is that we can learn a more constrained prior without having to sacrifice its generality to a specific action type. A single prior is learnt using all actions in the Human Eva dataset [9] and we provide quantitative results for images selected across all action categories and subjects, captured from differing viewpoints.

Published in:

Pattern Recognition (ICPR), 2010 20th International Conference on

Date of Conference:

23-26 Aug. 2010