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This paper addresses a strategy for 3D human motion recovery from monocular image. We advocate the use of Gaussian Process Dynamical Model (GPDM) for learning human pose and motion priors for 3D people tracking. With the prior learned from GPDM, we integrate our approach into a Bayesian tracking framework of condensation. During the off-line training step, a GPDM provides the reversible mappings between...
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