The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Topologically-constrained latent variable models
Abstract
In dimensionality reduction approaches, the data are typically embedded in a Euclidean latent space. However for some data sets this is inappropriate. For example, in human motion data we expect latent spaces that are cylindrical or a toroidal, that are poorly captured with a Euclidean space. In this paper, we present a range of approaches for embedding data in a non-Euclidean latent space. Our focus is the Gaussian Process latent variable model. In the context of human motion modeling this allows us to (a) learn models with interpretable latent directions enabling, for example, style/content separation, and (b) generalise beyond the data set enabling us to learn transitions between motion styles even though such transitions are not present in the data.
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“Spatio-temporal graphs (st-graphs) are a popular [39,37,4,11,32,65,22] general tool for representing such high-level spatio-temporal structures.”
From Structural-RNN: Deep Learning on Spatio-Temporal Graphs (2016) · cites this paper as [32]
Citations
Cites
Nonlinear Dimensionality Reduction by Locally Linear Embedding
Roweis, S. T., & Saul, L. K. (2000) · Science · doi:10.1126/science.290.5500.2323 · Cited by 14,965
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
Kovar, L., Gleicher, M. L., & Pighin, F. P. (2008) · ACM SIGGRAPH Courses · doi:10.1145/1401132.1401202 · Cited by 1,073
Gaussian Process Dynamical Models for Human Motion
Wang, J. M., Fleet, D. J., & Hertzmann, A. (2007) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1167 · Cited by 1,046
Self-citationProbabilistic Non-linear Principal Component Analysis with Gaussian Process Latent Variable Models
Lawrence, N. D. (2005) · Cited by 906
Self-citationStyle-based inverse kinematics
Grochow, K., Martin, S. L., Hertzmann, A., & Popović, Z. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015755 · Cited by 650
Learning a kernel matrix for nonlinear dimensionality reduction
Weinberger, K. Q., Sha, F., & Saul, L. K. (2004) · doi:10.1145/1015330.1015345 · Cited by 526
3D People Tracking with Gaussian Process Dynamical Models
Urtasun, R., Fleet, D. J., & Fua, P. (2006) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2006.15 · Cited by 444
Self-citationInferring 3D body pose from silhouettes using activity manifold learning
Elgammal, A., & Lee, C.-S. (2004) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2004.1315230 · Cited by 421
Gaussian Process Dynamical Models
Wang, J., Hertzmann, A., & Blei, D. M. (2005) · Cited by 418
Cited by
Sorted by citations
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
Recurrent Network Models for Human Dynamics
Fragkiadaki, K., Levine, S., Felsen, P., & Malik, J. (2015) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.494 · Cited by 933
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
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
Long-Term Human Motion Prediction with Scene Context
Cao, Z., Gao, H., Mangalam, K., Cai, Q.-Z., Vo, M., & Malik, J. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58452-8_23 · Cited by 246
Metadata
- Authors
- Urtasun, R., Fleet, D. J., Geiger, A., Popović, J., Darrell, T. J., & Lawrence, N. D.
- Affiliations
- MIT Computer Science and Artificial Intelligence Laboratory (US)
- University of Toronto (CA)
- Karlsruhe Institute of Technology (DE)
- University of Manchester (GB)
- Venue
- Proceedings of the 25th international conference on Machine learning - ICML '08
- Publisher
- ACM Press
- Published
- 2008
- Pages
- 1080-1087
- OpenAlex
- W2171461439
- Cited by
- 125
- References
- 12
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 2 · 2023: 4 · 2022: 3 · 2021: 6 · 2020: 8 · 2019: 7 · 2018: 7 · 2017: 8 · 2016: 3 · 2015: 8 · 2014: 7 · 2013: 13 · 2012: 11
- FWCI
- 8.90
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- International Conference on Machine Learning (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Gaussian Processes and Bayesian Inference
- Physical Sciences › Computer Science › Computer Graphics and Computer-Aided Design › Human Motion and Animation
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- Gaussian process latent variable model, dimensionality reduction, human motion modeling
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