Wang, J.M., Fleet, D.J. and Hertzmann, A.
Gaussian Process Dynamical Models for Human Motion.
IEEE Transactions on Pattern Analysis and Machine Intelligence,
We introduce Gaussian process dynamical models (GPDM) 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, and a map from the latent space to an observation space.
We marginalize out the model parameters in closed-form, using Gaussian
process (GP) 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.
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