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Gaussian Process Dynamical Models for Human Motion

Wang, J.M., Fleet, D.J., Hertzmann, A.·2008·IEEE Transactions on Pattern Analysis and Machine Intelligence

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.

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Metadata

Authors
Wang, J. M., Fleet, D. J., & Hertzmann, A.
Venue
IEEE Transactions on Pattern Analysis and Machine Intelligence
Published
2008-02
Volume
30
Issue
2
Pages
283-298
ISSN
0162-8828 (ISSN-L)
OpenAlex
W2124609748
Cited by
1,020
References
5
FWCI
42.95
Type
journal article
Language
en
Open access
closed

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