The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
MoSh: motion and shape capture from sparse markers
Open accessAbstract
Marker-based motion capture (mocap) is widely criticized as producing lifeless animations. We argue that important information about body surface motion is present in standard marker sets but is lost in extracting a skeleton. We demonstrate a new approach called MoSh (Motion and Shape capture), that automatically extracts this detail from mocap data. MoSh estimates body shape and pose together using sparse marker data by exploiting a parametric model of the human body. In contrast to previous work, MoSh solves for the marker locations relative to the body and estimates accurate body shape directly from the markers without the use of 3D scans; this effectively turns a mocap system into an approximate body scanner. MoSh is able to capture soft tissue motions directly from markers by allowing body shape to vary over time. We evaluate the effect of different marker sets on pose and shape accuracy and propose a new sparse marker set for capturing soft-tissue motion. We illustrate MoSh by recovering body shape, pose, and soft-tissue motion from archival mocap data and using this to produce animations with subtlety and realism. We also show soft-tissue motion retargeting to new characters and show how to magnify the 3D deformations of soft tissue to create animations with appealing exaggerations.
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Cites
Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation, Second Edition
Griewank, A., & Walther, A. (2008) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9780898717761 · Cited by 1,529
SCAPE: 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
Eulerian video magnification for revealing subtle changes in the world
Wu, H.-Y., Rubinstein, M., Shih, E., Guttag, J., Durand, F., & Freeman, W. (2012) · ACM Transactions on Graphics · doi:10.1145/2185520.2185561 · Cited by 1,357
The space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM SIGGRAPH 2003 Papers · doi:10.1145/1201775.882311 · Cited by 913
Phase-based video motion processing
Wadhwa, N., Rubinstein, M., Durand, F., & Freeman, W. T. (2013) · ACM Transactions on Graphics · doi:10.1145/2461912.2461966 · Cited by 853
The space of human body shapes: reconstruction and parameterization from range scans
Allen, B., Curless, B., & Popović, Z. (2003) · ACM Transactions on Graphics · doi:10.1145/882262.882311 · Cited by 723
FAUST: Dataset and Evaluation for 3D Mesh Registration
Bogo, F., Romero, J., Loper, M., & Black, M. J. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2014.491 · Cited by 689
Self-citationPerformance capture from sparse multi-view video
de Aguiar, E., Stoll, C., Theobalt, C., Ahmed, N., Seidel, H.-P., & Thrun, S. (2008) · ACM Transactions on Graphics · doi:10.1145/1360612.1360697 · Cited by 535
Surface Capture for Performance-Based Animation
Starck, J., & Hilton, A. (2007) · IEEE Computer Graphics and Applications · doi:10.1109/mcg.2007.68 · Cited by 452
Robinette, K. M., Blackwell, S., Daanen, H., Boehmer, M., & Fleming, S. (2002) · doi:10.21236/ada406704 · Cited by 260
Research works printed but not linked: 5
- de Aguiar E., Proceedings IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), 1--8.
- 10.5555/1455489 · doi:10.5555/1455489
- 10.5555/1338439.1338550 · doi:10.5555/1338439.1338550
- 10.1109/TVCG.2013.80 · doi:10.1109/tvcg.2013.80
- Nocedal J. and Wright S. J. 2006. Numerical Optimization 2nd ed. Springer New York. Nocedal J. and Wright S. J. 2006. Numerical Optimization 2nd ed. Springer New York.
Cited by
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-citationExpressive 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-citationAMASS: Archive of Motion Capture As Surface Shapes
Mahmood, N., Ghorbani, N., Troje, N. F., Pons-Moll, G., & Black, M. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2019.00554 · Cited by 1,323
Self-citationLearning from Synthetic Humans
Varol, G., Romero, J., Martin, X., Mahmood, N., Black, M. J., Laptev, I., & Schmid, C. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.492 · Cited by 908
Self-citationSMPL: A Skinned Multi-Person Linear Model
Loper, M., Mahmood, N., Romero, J., Pons-Moll, G., & Black, M. J. (2023) · Seminal Graphics Papers: Pushing the Boundaries, Volume 2 · doi:10.1145/3596711.3596800 · Cited by 768
Self-citation
Metadata
- Authors
- Loper, M., Mahmood, N., & Black, M. J.
- Venue
- ACM Transactions on Graphics
- Published
- 2014-11-19
- Volume
- 33
- Issue
- 6
- Pages
- 1-13
- ISSN
- 0730-0301 (ISSN-L)
- 1557-7368
- OpenAlex
- W2122633688
- Cited by
- 344
- References
- 26
- FWCI
- 16.00
- Type
- journal article
- Language
- en
- Open access
- bronze
- Funders
- National Science Foundation (EIA-0196217)
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