The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Modeling the constraints of human hand motion
Abstract
Hand motion capture is one of the most important parts of gesture interfaces. Many current approaches to this task generally involve a formidable nonlinear optimization problem in a large search space. Motion capture can be achieved more cost-efficiently when considering the motion constraints of a hand. Although some constraints can be represented as equalities or inequalities, there exist many constraints which cannot be explicitly represented. In this paper, we propose a learning approach to model the hand configuration space directly. The redundancy of the configuration space can be eliminated by finding a lower-dimensional subspace of the original space. Finger motion is modeled in this subspace based on the linear behavior observed in the real motion data collected by a CyberGlove. Employing the constrained motion model, we are able to efficiently capture finger motion from video inputs. Several experiments show that our proposed model is helpful for capturing articulated motion.
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Citations
Cites
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Physically based motion transformation
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Model-based tracking of self-occluding articulated objects
Rehg, J. M., & Kanade, T. (2002) · Proceedings of IEEE International Conference on Computer Vision · doi:10.1109/iccv.1995.466882 · Cited by 392
Model-based analysis of hand posture
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Efficient generation of motion transitions using spacetime constraints
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Inferring body pose without tracking body parts
Rosales, R., & Sclaroff, S. (2002) · Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662) · doi:10.1109/cvpr.2000.854946 · Cited by 170
Real-time animation of realistic virtual humans
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Cited by
Learning Joint Reconstruction of Hands and Manipulated Objects
Hasson, Y., Varol, G., Tzionas, D., Kalevatykh, I., Black, M. J., Laptev, I., & Schmid, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01208 · Cited by 513
A low cost instrumented glove for extended monitoring and functional hand assessment
Simone, L. K., Sundarrajan, N., Luo, X., Jia, Y., & Kamper, D. G. (2007) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2006.09.021 · Cited by 154
High-Fidelity Grasping in Virtual Reality using a Glove-based System
Liu, H., Zhang, Z., Xie, X., Zhu, Y., Liu, Y., Wang, Y., & Zhu, S.-C. (2019) · 2019 International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2019.8794230 · Cited by 73
A glove-based system for studying hand-object manipulation via joint pose and force sensing
Liu, H., Xie, X., Millar, M., Edmonds, M., Gao, F., Zhu, Y., Santos, V. J., Rothrock, B., & Zhu, S.-C. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2017.8206575 · Cited by 60
Metadata
- Authors
- Lin, J., Wu, Y., & Huang, T. S.
- Venue
- Proceedings Workshop on Human Motion
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-11
- Pages
- 121-126
- OpenAlex
- W1815774045
- Cited by
- 270
- References
- 9
- FWCI
- 4.60
- Type
- conference paper
- Language
- en
- Open access
- closed
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