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Gaussian Process Dynamical Models for Human Motion
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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Cited by
Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks
Gupta, A., Johnson, J., Fei-Fei, L., Savarese, S., & Alahi, A. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00240 · Cited by 2,466
Structural-RNN: Deep Learning on Spatio-Temporal Graphs
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Learning Social Etiquette: Human Trajectory Understanding In Crowded Scenes
Robicquet, A., Sadeghian, A., Alahi, A., & Savarese, S. (2016) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-46484-8_33 · Cited by 927
UniMiB SHAR: A Dataset for Human Activity Recognition Using Acceleration Data from Smartphones
Micucci, D., Mobilio, M., & Napoletano, P. (2017) · Applied Sciences · doi:10.3390/app7101101 · Cited by 596
Capturing the human figure through a wall
Adib, F., Hsu, C.-Y., Mao, H., Katabi, D., & Durand, F. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818072 · Cited by 443
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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