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We introduce Gaussian process dynamical models (GPDM) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensionalmotion capture data. A GPDM is a latent variable model. It comprises a low-dimensional latent space with associated dynamics, and a map from the latent space to an observation space. We marginalize out the model parameters in closed-form, using Gaussian process priors for both the dynamics and the observation mappings. This results in a non-parametric model for dynamical systems that accounts for uncertainty in the model. We demonstrate the approach, and compare four learning algorithms on human motion capture data in which each pose is 50-dimensional. Despite the use of small data sets, the GPDM learns an effective representation of the nonlinear dynamics in these spaces.
machine learning, motion, tracking, animation, stochastic processes, time series analysis
David J. Fleet, Aaron Hertzmann, Jack M. Wang, "Gaussian Process Dynamical Models for Human Motion", IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 30, no. , pp. 283-298, February 2008, doi:10.1109/TPAMI.2007.1167
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