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Inferring 3D body pose from silhouettes using activity manifold learning
Abstract
We aim to infer 3D body pose directly from human silhouettes. Given a visual input (silhouette), the objective is to recover the intrinsic body configuration, recover the viewpoint, reconstruct the input and detect any spatial or temporal outliers. In order to recover intrinsic body configuration (pose) from the visual input (silhouette), we explicitly learn view-based representations of activity manifolds as well as learn mapping functions between such central representations and both the visual input space and the 3D body pose space. The body pose can be recovered in a closed form in two steps by projecting the visual input to the learned representations of the activity manifold, i.e., finding the point on the learned manifold representation corresponding to the visual input, followed by interpolating 3D pose.
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Cited by
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
Metadata
- Authors
- Elgammal, A., & Lee, C.-S.
- Venue
- Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004.
- Publisher
- IEEE
- Published
- 2004-11-12
- Volume
- 2
- Pages
- 681-688
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2152386463
- Cited by
- 416
- References
- 0
- FWCI
- 24.04
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Inferring 3D body pose from silhouettes using activity manifold learning. https://app.cochat.ai/papers/10.1109/cvpr.2004.1315230
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- Elgammal, A., & Lee, C.-S. (2004). Inferring 3D body pose from silhouettes using activity manifold learning. Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2004. CVPR 2004. https://doi.org/10.1109/cvpr.2004.1315230
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
