Gaussian process dynamical models for human motion
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, a...
| Publié dans: | IEEE transactions on pattern analysis and machine intelligence. - 1979. - 30(2008), 2 vom: 15. Feb., Seite 283-98 |
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| Auteur principal: | |
| Autres auteurs: | , |
| Format: | Article |
| Langue: | English |
| Publié: |
2008
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| Accès à la collection: | IEEE transactions on pattern analysis and machine intelligence |
| Sujets: | Journal Article Research Support, Non-U.S. Gov't Research Support, U.S. Gov't, Non-P.H.S. |
| Résumé: | 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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| Description: | Date Completed 12.03.2008 Date Revised 18.03.2022 published: Print ErratumIn: IEEE Trans Pattern Anal Mach Intell. 2008 Jun;30(6):1118 Citation Status MEDLINE |
| ISSN: | 1939-3539 |