Human Motion

Marginal Nonlinear Dynamic System (MNDS) [1]

The human figure exhibits complex and rich dynamic behavior that is both nonlinear and time-varying. To automate the process of motion modeling we consider a class of learned dynamic models cast in the framework of dynamic Bayesian networks (DBNs) applied to analysis and tracking of the human figure. We are especially interested in learning dynamic models from motion capture data using DBN formalisms and dimensionality reduction methods. We have been explored the role of dynamics in dimensionality reduction problems and developed a statistical approach to human motion modeling to utilize this important factor.

We propose a new family of marginal auto-regressive (MAR) models that describe the space of all stable auto-regressive sequences, regardless of their specific dynamics. We apply the MAR class of models as sequence priors in probabilistic sequence subspace embedding problems. In particular, we consider a Gaussian process latent variable approach to dimensionality reduction.

Marginal Auto-Regressive (MAR) Model

Marginal Nonlinear Dynamic System (MNDS) Model

Experiment of Synthetic Sequence Data

Experiment on Human Motion Data

Human Motion Modeling using MNDS

Results on Synthetic Sequences

Tracking resuls for real sequence ( animated GIF)


    [1] K. Moon and V. Pavlovic. “Impact of Dynamics on Subspace Embedding and Tracking of Sequences”. IEEE Conf. Computer Vision and Pattern Recognition. 2006. pp. 198-205.


  • GPLVM software package is provided by N. Lawrence –
  • The 3D human model and the Maya binaries provided by the authors of “ Discriminative Density Propagation for 3D Human Motion Estimation ” (C. Sminchisescu, A. Kanaujia, Z. Li, D. Metaxas), IEEE CVPR ’05.

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