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Modeling Human Motion Using Binary Latent Variables
Abstract
We propose a non-linear generative model for human motion data that uses an undirected model with binary latent variables and real-valued “visible ” variables that represent joint angles. The latent and visible variables at each time step receive directed connections from the visible variables at the last few time-steps. Such an architecture makes on-line inference efficient and allows us to use a simple approximate learning procedure. After training, the model finds a single set of parameters that simultaneously capture several different kinds of motion. We demonstrate the power of our approach by synthesizing various motion sequences and by performing on-line filling in of data lost during motion capture. Website:
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Gradient-based learning applied to document recognition
Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Self-citationTraining Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“Maximum likelihood learning is slow in an RBM but learning st ill works well if we approximately follow the gradient of another function called the contrast ive divergence[9].”
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(2010) · International Conference on Machine Learning · Cited by 13,995
Learning Deep Architectures for AI
Bengio, Y. (2009) · now publishers, Inc. eBooks · doi:10.1561/9781601982957 · Cited by 4,941
Reservoir computing approaches to recurrent neural network training
Lukoševičius, M., & Jaeger, H. (2009) · Computer Science Review · doi:10.1016/j.cosrev.2009.03.005 · Cited by 2,956
A Practical Guide to Training Restricted Boltzmann Machines
Hinton, G. E. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-35289-8_32 · Cited by 2,852
Self-citationLee, H., Grosse, R., Ranganath, R., & Ng, A. Y. (2009) · Proceedings of the 26th Annual International Conference on Machine Learning · doi:10.1145/1553374.1553453 · Cited by 2,412
Metadata
- Authors
- Taylor, G. W., Hinton, G. E., & Roweis, S. T.
- Venue
- Advances in Neural Information Processing Systems 19
- Publisher
- The MIT Press
- Published
- 2007-09-07
- Pages
- 1345-1352
- OpenAlex
- W2158164339
- Cited by
- 689
- References
- 3
- FWCI
- 54.70
- Type
- book chapter
- Language
- en
- Open access
- closed
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- Taylor, G. W., Hinton, G. E., & Roweis, S. T. (2007). Modeling Human Motion Using Binary Latent Variables. Advances in Neural Information Processing Systems 19. https://doi.org/10.7551/mitpress/7503.003.0173
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
