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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]
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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
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Li, R., Tian, T.-P., Sclaroff, S., & Yang, M. (2009) · International Journal of Computer Vision · doi:10.1007/s11263-009-0283-4 · Cited by 61
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Yu, P., Zhao, Y., Li, C., Yuan, J., & Chen, C. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-58577-8_2 · Cited by 46
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Gong, D., Zhao, X., & Medioni, G. G. (2012) · arXiv (Cornell University) · doi:10.48550/arxiv.1206.4624 · Cited by 42
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Wang, Y., Brubaker, M. A., Chaib-draa, B., & Urtasun, R. (2014) · Cited by 9
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Sminchisescu, C., Bo, L., Ionescu, C., & Kanaujia, A. (2011) · Springer eBooks · doi:10.1007/978-0-85729-997-0_12 · Cited by 7
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Self-citationProbabilistic Discriminative Dimensionality Reduction for Pose-Based Action Recognition
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Structure-guided manifold learning for video-based motion estimation
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Yu, J., Sun, J., Liu, S., & Luo, S. (2017) · Multimedia Tools and Applications · doi:10.1007/s11042-017-4847-y · Cited by 5
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Revisiting Gaussian process dynamical models
Zhao, J., & Sun, S. (2015) · International Conference on Artificial Intelligence · Cited by 4
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Ntouskos, V., Papadakis, P., & Pirri, F. (2013) · International Conference on Pattern Recognition Applications and Methods · doi:10.5220/0004268600870096 · Cited by 4
PACE: Data-Driven Virtual Agent Interaction in Dense and Cluttered Environments
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Exploring affordances in robot grasping through latent structure representation
Ek, C. H., Song, D., Huebner, K., & Kragić, D. (2010) · Bristol Research (University of Bristol) · Cited by 4
Kick-starting GPLVM optimization via a connection to metric MDS
Bitzer, S., & Williams, C. K. I. (2010) · Max Planck Digital Library · Cited by 4
Sparse summarization of robotic grasping data
Hjelm, M., Ek, C. H., Detry, R. J., Kjellström, H., & Kragić, D. (2013) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/icra.2013.6630707 · Cited by 3
Conditional neural relational inference for interacting systems
Ramos, J. A. C., Blondé, L., Armand, S., & Kalousis, A. (2021) · ArODES (HES-SO (https://www.hes-so.ch/)) · Cited by 3
A Manifold Representation as Common Basis for Action Production and Recognition
Steffen, J., Pardowitz, M., & Ritter, H. J. (2009) · Lecture notes in computer science · doi:10.1007/978-3-642-04617-9_76 · Cited by 3
Clustering-Based Latent Variable Models for Monocular Non-rigid 3D Shape Recovery
Wang, Q., Wang, F., Li, D., & Wang, X. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-09339-0_17 · Cited by 3
Infrared target tracking, recognition and segmentation using shape-aware level set
Gong, J., Fan, G., Havlicek, J., Fan, N., & Chen, D. (2013) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2013.6738676 · Cited by 3
Action-guided 3D Human Motion Prediction
Sun, J., Lin, Z., Han, X., Hu, J., Xu, J., & Zheng, W. (2021) · neural information processing systems · Cited by 3
Probabilistic Curve Learning: Coulomb Repulsion and the Electrostatic Gaussian ProcessPreprint
Wang, Y., & Dunson, D. B. (2015) · arXiv (Cornell University) · doi:10.48550/arxiv.1506.03768 · Cited by 3
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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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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