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Gaussian Process Dynamical Models for Human Motion Abstract We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motion capture data. A GPDM is a latent variable model. It comprises a low dimensional latent space with associated dynamics, as well as a map from the latent space