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- Publisher Website: 10.1145/1273496.1273619
- Scopus: eid_2-s2.0-34547979938
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Conference Paper: Multifactor Gaussian process models for style-content separation
Title | Multifactor Gaussian process models for style-content separation |
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Authors | |
Issue Date | 2007 |
Citation | ACM International Conference Proceeding Series, 2007, v. 227, p. 975-982 How to Cite? |
Abstract | We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functions. By marginalizing over the weights, we obtain a multifactor form of the Gaussian process latent variable model. In this model, each factor is kernelized independently, allowing nonlinear mappings from any particular factor to the data. We learn models for human locomotion data, in which each pose is generated by factors representing the person's identity, gait, and the current state of motion. We demonstrate our approach using time-series prediction, and by synthesizing novel animation from the model. |
Persistent Identifier | http://hdl.handle.net/10722/192708 |
DC Field | Value | Language |
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dc.contributor.author | Wang, JM | en_US |
dc.contributor.author | Fleet, DJ | en_US |
dc.contributor.author | Hertzmann, A | en_US |
dc.date.accessioned | 2013-11-20T04:56:54Z | - |
dc.date.available | 2013-11-20T04:56:54Z | - |
dc.date.issued | 2007 | en_US |
dc.identifier.citation | ACM International Conference Proceeding Series, 2007, v. 227, p. 975-982 | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/192708 | - |
dc.description.abstract | We introduce models for density estimation with multiple, hidden, continuous factors. In particular, we propose a generalization of multilinear models using nonlinear basis functions. By marginalizing over the weights, we obtain a multifactor form of the Gaussian process latent variable model. In this model, each factor is kernelized independently, allowing nonlinear mappings from any particular factor to the data. We learn models for human locomotion data, in which each pose is generated by factors representing the person's identity, gait, and the current state of motion. We demonstrate our approach using time-series prediction, and by synthesizing novel animation from the model. | en_US |
dc.language | eng | en_US |
dc.relation.ispartof | ACM International Conference Proceeding Series | en_US |
dc.title | Multifactor Gaussian process models for style-content separation | en_US |
dc.type | Conference_Paper | en_US |
dc.description.nature | link_to_subscribed_fulltext | en_US |
dc.identifier.doi | 10.1145/1273496.1273619 | en_US |
dc.identifier.scopus | eid_2-s2.0-34547979938 | en_US |
dc.identifier.volume | 227 | en_US |
dc.identifier.spage | 975 | en_US |
dc.identifier.epage | 982 | en_US |