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The Role of Manifold Learning in Human Motion Analysis

  • Ahmed Elgammal
  • Chan-Su Lee
Part of the Computational Imaging and Vision book series (CIVI, volume 36)

The human body is an articulated object with a high number of degrees of freedom. Despite the high dimensionality of the configuration space, many human motion activities lie intrinsically on low-dimensional manifolds. Although the intrinsic body configuration manifolds might be very low in dimensionality, the resulting appearance manifolds are challenging to model given various aspects that affect the appearance such as the shape and appearance of the person performing the motion, or variation in the viewpoint, or illumination. Our objective is to learn representations for the shape and the appearance of moving (dynamic) objects that support tasks such as synthesis, pose recovery, reconstruction, and tracking. We studied various approaches for representing global deformation manifolds that preserve their geometric structure. Given such representations, we can learn generative models for dynamic shape and appearance. We also address the fundamental question of separating style and content on nonlinear manifolds representing dynamic objects. We learn factorized generative models that explicitly decompose the intrinsic body configuration (content) as a function of time from the appearance/shape (style factors) of the person performing the action as time-invariant parameters. We show results on pose recovery, body tracking, gait recognition, as well as facial expression tracking and recognition.

Keywords

Facial Expression Human Motion Configuration Space Style Factor Dynamic Object 
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 2008

Authors and Affiliations

  • Ahmed Elgammal
    • 1
  • Chan-Su Lee
    • 1
  1. 1.Department of Computer ScienceRutgers UniversityPiscatawayUSA

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