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 to an observation space. We marginalize out the model parameters
in closed form by using Gaussian process priors for both the dynamical
and the observation mappings. This results in a nonparametric 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.
People
Papers
Wang, J. M., Fleet, D. J., Hertzmann, A. Gaussian Process Dynamical Models for Human Motion. In IEEE Transactions on Pattern Recognition and Machine Intelligence. February, 2008. pp. 283-298.
Errata: Figures 7 and 8 on page 292 are incorrectly printed, please find the corrected figures appended to the end of the pdf.
Note: Over the years, a few people have asked me about how Equation (10) is derived.
Wang, J. M., Fleet, D. J., Hertzmann, A. Gaussian Process Dynamical Models. In Proc. NIPS 2005. December, 2005. Vancouver, Canada. pp. 1441-1448.
Errata: Figures 7 and 8 on page 292 are incorrectly printed, please find the corrected figures appended to the end of the pdf.
Note: Over the years, a few people have asked me about how Equation (10) is derived.
Wang, J. M., Fleet, D. J., Hertzmann, A. Gaussian Process Dynamical Models. In Proc. NIPS 2005. December, 2005. Vancouver, Canada. pp. 1441-1448.
Software
A version of this work has been implemented by Neil Lawrence as an extension to his GP-LVM software packages. Visit his Gaussian process software page for downloading information.
The current version of our GPDM code, which includes code that generate HMC samples and other mocap utils, but are not nearly as organized as Neil's code.
The current version of our GPDM code, which includes code that generate HMC samples and other mocap utils, but are not nearly as organized as Neil's code.
MAIN REFERENCE
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