Gaussian Process Dynamical Models
This project introduces Gaussian process dynamical models (GPDM) for
nonlinear time series analysis, and aims to explore potential applications to people tracking and data-driven animation. A GPDM 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, which amounts
to using Gaussian process (GP) priors for both the dynamics and the
observation mappings.
This results in a nonparametric model for dynamical systems that accounts
for uncertainty in the model. We demonstrate the approach on human motion
capture data in which each pose is 62-dimensional.
Despite the use of small data sets, the GPDM learns an effective
representation of the nonlinear dynamics in these spaces.
Papers
Wang, J. M., Fleet, D. J., Hertzmann, A. Gaussian Process Dynamical Models for Human Motion. In IEEE Trans. PAMI. February, 2008. pp. 283-298.
Errata: Figures 7 and 8 on page 292 are incorrectly printed, please find the corrected figures here.
Thanks to Neil Lawrence for pointing this out.
Wang, J. M., Fleet, D. J., Hertzmann, A. Gaussian Process Dynamical Models. In Proc. NIPS 2005. December, 2005. Vancouver, Canada. pp. 1441-1448. [bibtex]
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.
Demos
Demos contain animated gifs that link to corresponding
QuickTime movies (some over 10 MB); jpegs link to higher-resolution jpeg
images.
3D GPDM
2D GPDM
Missing Data Demo
Golf Demo
People
Jack Wang
David Fleet
Aaron Hertzmann
Acknowledgements
This work made use of Neil Lawrence's publicly-available GPLVM code, the CMU mocap database, and Joe Conti's volume visualization code.
This research was supported by the National Sciences and Engineering Research Council of Canada (NSERC), and the Canadian Institute for Advanced Research (CIAR).