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Conference Paper

Articulated People Detection and Pose Estimation: Reshaping the Future

MPS-Authors
http://pubman.mpdl.mpg.de/cone/persons/resource/persons45196

Pishchulin,  Leonid
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons44690

Jain,  Arjun
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons71836

Andriluka,  Mykhaylo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45618

Thormaehlen,  Thorsten
Computer Graphics, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Citation

Pishchulin, L., Jain, A., Andriluka, M., Thormaehlen, T., & Schiele, B. (2012). Articulated People Detection and Pose Estimation: Reshaping the Future. In S. Belongie, A. Blake, J. Luo, & A. Yuille (Eds.), 2012 IEEE Conference on Computer Vision and Pattern Recognition (pp. 3178-3185). Piscataway, NJ: IEEE. doi:10.1109/CVPR.2012.6248052.


Cite as: http://hdl.handle.net/11858/00-001M-0000-0013-F79E-A
Abstract
State-of-the-art methods for human detection and pose estimation require many training samples for best performance. While large, manually collected datasets exist, the captured variations w.r.t. appearance, shape and pose are often uncontrolled thus limiting the overall performance. In order to overcome this limitation we propose a new technique to extend an existing training set that allows to explicitly control pose and shape variations. For this we build on recent advances in computer graphics to generate samples with realistic appearance and background while modifying body shape and pose. We validate the effectiveness of our approach on the task of articulated human detection and articulated pose estimation. We report close to state of the art results on the popular Image Parsing human pose estimation benchmark and demonstrate superior performance for articulated human detection. In addition we define a new challenge of combined articulated human detection and pose estimation in real-world scenes.