Conclusions

Bayesian formulation for tracking 3D human figures

using monocular image information.
• Generative model of image appearance.
• Non-linear model represents ambiguities, singularities
  occlusion, etc - sampled representation of posterior.
• Particle filtering for incremental estimation.
• Automatic learning of cyclic motion prior.

Rich framework for modeling the complexity of

human motion.

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