The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
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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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
A Global Geometric Framework for Nonlinear Dimensionality Reduction
Tenenbaum, J. B., Silva, V. D., & Langford, J. C. (2000) · Science · doi:10.1126/science.290.5500.2319 · Cited by 13,585
CONDENSATION—Conditional Density Propagation for Visual Tracking
Isard, M., & Blake, A. (1998) · International Journal of Computer Vision · doi:10.1023/a:1008078328650 · Cited by 4,886
A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants
Neal, R. M., & Hinton, G. E. (1998) · Learning in Graphical Models · doi:10.1007/978-94-011-5014-9_12 · Cited by 2,187
Learning and inference in parametric switching linear dynamic systems
Oh, S. M., Rehg, J. M., Balch, T., & Dellaert, F. (2005) · Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1 · doi:10.1109/iccv.2005.135 · Cited by 40
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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
Jain, A., Zamir, A. R., Savarese, S., & Saxena, A. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.573 · Cited by 1,133
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
Convolutional Sequence to Sequence Model for Human Dynamics
Li, C., Zhang, Z., Lee, W. S., & Lee, G. H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00548 · Cited by 364
Adversarial Geometry-Aware Human Motion Prediction
Gui, L.-Y., Wang, Y.-X., Liang, X., & Moura, J. M. F. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01225-0_48 · Cited by 276
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Shu, X., Zhang, L., Qi, G.-J., Liu, W., & Tang, J. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3050918 · Cited by 276
A Neural Temporal Model for Human Motion Prediction
Gopalakrishnan, A., Mali, A., Kifer, D., Giles, L., & Ororbia, A. G. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01239 · Cited by 185
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Probabilistic movement modeling for intention inference in human–robot interaction
Wang, Z., Mülling, K., Deisenroth, M. P., Ben Amor, H., Vogt, D., Schölkopf, B., & Peters, J. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913478447 · Cited by 171
Topologically-constrained latent variable models
Urtasun, R., Fleet, D. J., Geiger, A., Popović, J., Darrell, T. J., & Lawrence, N. D. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390292 · Cited by 125
Self-citationProbabilistic real-time user posture tracking for personalized robot-assisted dressing
Zhang, F., Cully, A., & Demiris, Y. (2019) · IEEE Transactions on Robotics · doi:10.1109/tro.2019.2904461 · Cited by 70
Gaussian Process Gauss–Newton for non-parametric simultaneous localization and mapping
Tong, C. H., Furgale, P., & Barfoot, T. D. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913478672 · Cited by 68
Fast and deep deformation approximations
Bailey, S. W., Otte, D., Dilorenzo, P., & O'Brien, J. F. (2018) · ACM Transactions on Graphics · doi:10.1145/3197517.3201300 · Cited by 61
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Sang, H.-F., Chen, Z.-Z., & He, D.-K. (2020) · Multimedia Tools and Applications · doi:10.1007/s11042-019-08269-7 · Cited by 42
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Li, Q., Chalvatzaki, G., Peters, J., & Wang, Y. (2021) · 2021 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra48506.2021.9561540 · Cited by 29
Skip-attention encoder–decoder framework for human motion prediction
Zhang, R., Shu, X., Yan, R., Zhang, J., & Song, Y. (2022) · Multimedia Systems · doi:10.1007/s00530-021-00807-4 · Cited by 13
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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