We propose a statistical formulation for 2D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to infer pose parameters from image cues such as appearance, shape, edge, and color. From a set of hand labeled images, we accumulate prior knowledge of 2D body shapes by learning their low-dimensional representations for inference of pose parameters. A data driven belief propagation Monte Carlo algorithm, utilizing importance sampling functions built from bottom-up visual cues, is proposed for efficient probabilistic inference. Contrasted to the few sequential statistical formulations in the literature, our algorithm integrates both top-down as well as bottom-up reasoning mechanisms, and can carry out the inference tasks in parallel. Experimental results demonstrate the potency and effectiveness of the proposed algorithm in estimating 2D human pose from single images.


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    Title :

    Learning to estimate human pose with data driven belief propagation


    Contributors:
    Gang Hua, (author) / Ming-Hsuan Yang, (author) / Ying Wu, (author)


    Publication date :

    2005-01-01


    Size :

    586506 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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