title: Gaussian process dynamical models for human motion.
abstract: 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.
authors: Department of Computer Science, University of Toronto, 40 St. George Street, Toronto, Ontario M5S 2E4 Canada. jmwang@dgp.toronto.edu
authors: Jack M
authors: JM
authors: Wang
authors: David J
authors: DJ
authors: Fleet
authors: Aaron
authors: A
authors: Hertzmann
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001698:Biomedical Engineering; D003198:Computer Simulation; D005684:Gait / Q000502:physiology; D006801:Humans; D016014:Linear Models; D008954:Models, Biological; D009068:Movement / Q000502:physiology*; D017711:Nonlinear Dynamics; D014741:Video Recording; D016138:Walking / Q000502:physiology