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The REPARTI Seminars at Université Laval are held on Fridays at 11:30 a.m.
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REPARTI

MIVIM

Apr 11 2014 11:30AM

Yali Wang
Laboratoire DAMAS
Département d'informatique et de génie logiciel
Université Laval


Human Motion Prediction with Gaussian Process Dynamic Model



Abstract

Human Motion Prediction is a challenging computer vision task. First of all, the motion capture data sets are high-dimensional. Secondly, the complicated motions are often reflect multi-modal. Recently, a Gaussian process dynamic model (GPDM) [J. M. Wang, D. J. Fleet and A. Hertzmann, NIPS 2005] has been proposed for human motion tracking. In GPDM, Gaussian processes (GPs) are used to construct the nonlinear mapping in both transition and likelihood equations of the state space model. The latent states and GP hyper-parameters can then be efficiently learned from the closed-form posterior by using gradient-based optimization. In this presentation, I will mainly focus on this GPDM model, and then propose an extension of GPDM to capture the complicated motions.




     
   
   

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