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Last edited by WorkBot
January 24, 2010 | History

Gaussian process dynamical models for human motion 1 edition

Gaussian process dynamical models for human motion
Jack Meng-Chieh Wang

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Gaussian process dynamical models for human motion.

Published 2005 .
Written in English.

About the Book

This thesis introduces Gaussian process dynamical models (GPDMs) for nonlinear time series analysis. 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 leads to modeling both dynamics and observation mappings as Gaussian processes. This results in a nonparametric model for dynamical systems that accounts for uncertainty in the model. We train the model on human motion capture data in which each pose is 62-dimensional, and synthesize new motions by sampling from the posterior distribution. A comparison of forecasting results between different covariance functions and sampling methods is provided, and we demonstrate a simple application of GPDM on filling in missing data. Finally, to account for latent space uncertainty, we explore different priors settings on hyperparameters and show some preliminary GPDM learning results using a Monte Carlo expectation-maximization algorithm.

Edition Notes

Source: Masters Abstracts International, Volume: 44-02, page: 0948.

Advisor: A. Hertzmann.

Thesis (M.Sc.)--University of Toronto, 2005.

Electronic version licensed for access by U. of T. users.

GERSTEIN MICROTEXT copy on microfiche (1 microfiche).

The Physical Object

Pagination
78 leaves.
Number of pages
78

ID Numbers

Open Library
OL19216603M
ISBN 10
0494071958

History Created December 11, 2009 · 2 revisions Download catalog record: RDF / JSON

January 24, 2010 Edited by WorkBot add more information to works
December 11, 2009 Created by WorkBot add works page