Inferring 3D Body Pose from Uncalibrated Video

  • Xian-Jie Qiu
  • Zhao-Qi Wang
  • Shi-Hong Xia
  • Yong-Chao Sun
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4020)


Recovery of 3D body pose is a fundamental problem for human motion analysis in many applications such as motion capture, vision interface, visual surveillance, and gesture recognition. In this paper, we present a new image-based approach to infer 3D human structure parameters from uncalibrated video. The estimation is example based. First, we acquire a special motion database through an off-line motion capture process. Second, given uncalibrated motion video, we abstract the extrinsic parameters and then silhouettes database associated with 3D poses is built by projecting each data of the 3D motion database into 2D plane with the extrinsic parameters. Next, with the image silhouettes abstracted from video, the unknown structure parameters are inferred by performs a similarity search in the database of silhouettes using approach based on shape matching. That is, the 3D structure parameters whose 2D projective silhouette is the most similar to the 2D image silhouette are took as the 3D reconstruction structure. We use trampoline sport motion, an example of complex human motion, to demonstrate the effectiveness of our approach.


Motion Capture Gesture Recognition Moment Invariant Extrinsic Parameter Sport Video 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Xian-Jie Qiu
    • 1
    • 2
  • Zhao-Qi Wang
    • 1
  • Shi-Hong Xia
    • 1
  • Yong-Chao Sun
    • 1
    • 3
  1. 1.Institute of Computing TechnologyThe Chinese Academy of SciencesBeijingChina
  2. 2.Graduate SchoolThe Chinese Academy of SciencesBeijingChina
  3. 3.Institute of Computing TechnologyThe Chinese Academy of SciencesShenyangChina

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