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3D Reconstruction of Human Motion and Skeleton from Uncalibrated Monocular Video

  • Yen-Lin Chen
  • Jinxiang Chai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5994)

Abstract

This paper introduces a new model-based approach for simultaneously reconstructing 3D human motion and full-body skeletal size from a small set of 2D image features tracked from uncalibrated monocular video sequences. The key idea of our approach is to construct a generative human motion model from a large set of preprocessed human motion examples to constrain the solution space of monocular human motion tracking. In addition, we learn a generative skeleton model from prerecorded human skeleton data to reduce ambiguity of the human skeleton reconstruction. We formulate the reconstruction process in a nonlinear optimization framework by continuously deforming the generative models to best match a small set of 2D image features tracked from a monocular video sequence. We evaluate the performance of our system by testing the algorithm on a variety of uncalibrated monocular video sequences.

Keywords

Human Motion Camera Parameter Input Video Reference Motion Skeleton Model 
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 2010

Authors and Affiliations

  • Yen-Lin Chen
    • 1
  • Jinxiang Chai
    • 1
  1. 1.Department of Computer Science and EngineeringTexas A&M UniversityCollege StationUSA

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