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IJCV
2006

Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation

8 years 10 months ago
Combining Generative and Discriminative Models in a Framework for Articulated Pose Estimation
We develop a method for the estimation of articulated pose, such as that of the human body or the human hand, from a single (monocular) image. Pose estimation is formulated as a statistical inference problem, where the goal is to find a posterior probability distribution over poses as well as a maximum a posteriori (MAP) estimate. The method combines two modeling approaches, one discriminative and the other generative. The discriminative model consists of a set of mapping functions that are constructed automatically from a labeled training set of body poses and their respective image features. The discriminative formulation allows for modeling ambiguous, one-to-many mappings (through the use of multi-modal distributions) that may yield multiple valid articulated pose hypotheses from a single image. The generative model is defined in terms of a computer graphics rendering of poses. While the generative model offers an accurate way to relate observed (image features) and hidden (body po...
Rómer Rosales, Stan Sclaroff
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2006
Where IJCV
Authors Rómer Rosales, Stan Sclaroff
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