The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image
Open accessAbstract
We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.
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Cites
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & J, B. M. (2015) · ACM Transactions on Graphics · doi:10.1145/2816795.2818013 · Cited by 3,734
Self-citationHuman3.6M: Large Scale Datasets and Predictive Methods for 3D Human Sensing in Natural Environments
Ionescu, C., Papava, D., Olaru, V., & Sminchisescu, C. (2014) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2013.248 · Cited by 3,653 · cited 3×
“We perform the same analysis on the Human 3.6M dataset [18], which has a wider range of poses.”
MethodInfluentialSCAPE: shape completion and animation of people
Anguelov, D., Srinivasan, P., Koller, D., Thrun, S., Rodgers, J., & Davis, J. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073207 · Cited by 1,489
Inference on networks of mixtures for robust robot mapping
Olson, E., & Agarwal, P. (2013) · The International Journal of Robotics Research · doi:10.1177/0278364913479413 · Cited by 252
MoDeep: A Deep Learning Framework Using Motion Features for Human Pose Estimation
Jain, A., Tompson, J., LeCun, Y., & Bregler, C. (2015) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-16808-1_21 · Cited by 142
Cited by
Expressive Body Capture: 3D Hands, Face, and Body From a Single Image
Pavlakos, G., Choutas, V., Ghorbani, N., Bolkart, T., Osman, A. A., Tzionas, D., & Black, M. J. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01123 · Cited by 1,781
Self-citationRomero (2017) · doi:10.1145/3130800.3130883 · Cited by 1,056
Integral Human Pose Regression
Sun, X., Xiao, B., Wei, F., Liang, S., & Wei, Y. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_33 · Cited by 874
FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape From Single RGB Images
Zimmermann, C., Ceylan, D., Yang, J., Russell, B., Argus, M. J., & Brox, T. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00090 · Cited by 445
3D Menagerie: Modeling the 3D Shape and Pose of Animals
Zuffi, S., Kanazawa, A., Jacobs, D. W., & Black, M. J. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.586 · Cited by 381
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Metadata
- Authors
- Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer International Publishing
- Published
- 2016
- Pages
- 561-578
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2483862638
- Cited by
- 1,498
- References
- 5
- FWCI
- 38.65
- Type
- conference paper
- Language
- en
- Open access
- green
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- Bogo, F., Kanazawa, A., Lassner, C., Gehler, P., Romero, J., & Black, M. J. (2016). Keep It SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-319-46454-1_34
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