skip to main content
10.1145/1077534.1077546acmconferencesArticle/Chapter ViewAbstractPublication PagesvrstConference Proceedingsconference-collections
Article

Marker-free kinematic skeleton estimation from sequences of volume data

Published:10 November 2004Publication History

ABSTRACT

For realistic animation of an artificial character a body model that represents the character's kinematic structure is required. Hierarchical skeleton models are widely used which represent bodies as chains of bones with interconnecting joints. In video motion capture, animation parameters are derived from the performance of a subject in the real world. For this acquisition procedure too, a kinematic body model is required. Typically, the generation of such a model for tracking and animation is, at best, a semi-automatic process. We present a novel approach that estimates a hierarchical skeleton model of an arbitrary moving subject from sequences of voxel data that were reconstructed from multi-view video footage. Our method does not require a-priori information about the body structure. We demonstrate its performance using synthetic and real data.

References

  1. F. Banégas, M. Jaeger, D. Michelucci, and M. Roelens. The ellipsoidal skeleton in medical applications. In Proc. of the sixth ACM symposium on Solid modeling and applications, pages 30--38. ACM Press, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. A. Bottino and A. Laurentini. A silhouette-based technique for the reconstruction of human movement. CVIU, 83:79--95, 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. C. Bregler and J. Malik. Tracking people with twists and exponential maps. In Proc. of CVPR 98, pages 8--15, 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. R. Byrd, P. Lu, J. Nocedal, and C. Zhu. A limited memory algorithm for bound constrained optimization. SIAM J. Sci. Comp., 16(5):1190--1208, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. J. Carranza, C. Theobalt, M. Magnor, and H.-P. Seidel. Free-viewpoint video of human actors. In Proc. of SIGGRAPH'03, pages 569--577, 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. G. Cheung, B. S., and T. Kanada. Shape-from-silhouette of articulated objects and its use for human body kinematics estiamtion and motion capture. In Proc. of CVPR, 2003.]]Google ScholarGoogle Scholar
  7. K. Cheung, T. Kanade, J.-Y. Bouguet, and M. Holler. A real time system for robust 3D voxel reconstruction of human motions. In Proc. of CVPR, volume 2, pages 714--720, 2000.]]Google ScholarGoogle ScholarCross RefCross Ref
  8. L. Chevalier, F. Jaillet, and B. A. Segmentation and superquadric modeling of 3D objects. In Proc. of WSCG 2003, 2003.]]Google ScholarGoogle Scholar
  9. E. de Aguiar, C. Theobalt, M. Magnor, H. Theisel, and H.-P. Seidel. M3: Marker-free model reconstruction and motion tracking from 3d voxel data. In Proc. of Pacific Graphics'04. to appear, 2004.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. Q. Delamarre and O. Faugeras. 3D articulated models and multi-view tracking with silhouettes. In Proc. of ICCV 99, pages 716--721, 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. B. Deutscher, A. Blake, and I. Reid. Articulated body motion capture by annealed particle filtering. In Proc. of CVPR'00, 2000.]]Google ScholarGoogle ScholarCross RefCross Ref
  12. D. Gavrila. The visual analysis of human movement. CVIU, 73(1):82--98, January 1999.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. D. Gavrila and L. Davis. 3D model-based tracking of humans in action: A multi-view approach. In Proc. of CVPR 96, pages 73--80, 1996.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. I. Kakadiaris and D. Metaxas. 3D human body model acquisition from multiple views. In Proc. of ICCV'95, pages 618--623, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. S. Katz and A. Tal. Hierarchical mesh decomposition using fuzzy clustering and cuts. In Proc. of SIGGRAPH'03, pages 954--961, 2003.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. K. N. Kutulakos and S. M. Seitz. A theory of shape by space carving. Int. J. Comput. Vision, 38(3):199--218, 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. A. Leonardis, A. Jaklic, and F. Solina. Superquadrics for segmenting and modeling range data. IEEE PAMI, 19(11):1289--1295, 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. M. Leung and Y. Yang. First sight: A human body outline labeling system. PAMI, 17(4):359--379, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. S. Loncaric. A survey of shape analysis techniques. Pattern Recognition, 31(8):983--1001, 1998.]]Google ScholarGoogle ScholarCross RefCross Ref
  20. J. Luck and D. Small. Real-time markerless motion tracking using linked kinematic chains. In Proc. of CVPRIP02, 2002.]]Google ScholarGoogle Scholar
  21. A. Menache. Understanding Motion Capture for Computer Animation and Video Games. Morgan Kaufmann, 1995.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. I. Mikić, M. Triverdi, E. Hunter, and P. Cosman. Articulated body posture estimation from multicamera voxel data. In Proc. of CVPR, 2001.]]Google ScholarGoogle Scholar
  23. R. Plaenkers and P. Fua. Tracking and modeling people in video sequences. CVIU, 81(3):285--302, March 2001.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  24. W. H. Press, S. A. Teukolsky, W. T. Vetterling, and B. P. Flannery. Numerical recipes in C++. Cambridge University Press, 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  25. K. Rohr. Incremental recognition of pedestrians from image sequences. In Proc. of CVPR, pages 8--13, 1993.]]Google ScholarGoogle ScholarCross RefCross Ref
  26. M.-C. Silaghi, R. Plaenkers, R. Boulic, P. Fua, and D. Thalmann. Local and global skeleton fitting techniques for optical motion capture. In Modeling and Motion Capture Techniques for Virtual Environments, number 1537 in LNAI, No1537, pages 26--40. Springer, 1998.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. M. Sniedovich. Dynamic programming. Marcel Dekker, Inc., 1992.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  28. C. Theobalt, M. Li, M. Magnor, and H.-P. Seidel. A flexible and versatile studio for synchronized multi-view video recording. In Proc. of Vision, Video and Graphics, pages 9--16, 2003.]]Google ScholarGoogle Scholar
  29. C. Theobalt, M. Magnor, P. Schueler, and H.-P. Seidel. Combining 2D feature tracking and volume reconstruction for online video-based human motion capture. In Proc. of Pacific Graphics 2002, pages 96--103, 2002.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. C. Wren, A. Azarbayejani, T. Darrell, and A. Pentland. Pfinder: Real-time tracking of the human body. PAMI, 19(7):780--785, 1997.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. S. Yonemoto, D. Arita, and R. Taniguchi. Real-time human motion analysis and IK-based human figure control. In Proc. of IEEE Workshop on Human Motion, pages 149--154, 2000.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

Index Terms

  1. Marker-free kinematic skeleton estimation from sequences of volume data

                  Recommendations

                  Comments

                  Login options

                  Check if you have access through your login credentials or your institution to get full access on this article.

                  Sign in
                  • Published in

                    cover image ACM Conferences
                    VRST '04: Proceedings of the ACM symposium on Virtual reality software and technology
                    November 2004
                    226 pages
                    ISBN:1581139071
                    DOI:10.1145/1077534

                    Copyright © 2004 ACM

                    Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

                    Publisher

                    Association for Computing Machinery

                    New York, NY, United States

                    Publication History

                    • Published: 10 November 2004

                    Permissions

                    Request permissions about this article.

                    Request Permissions

                    Check for updates

                    Qualifiers

                    • Article

                    Acceptance Rates

                    Overall Acceptance Rate66of254submissions,26%

                    Upcoming Conference

                    VRST '24

                  PDF Format

                  View or Download as a PDF file.

                  PDF

                  eReader

                  View online with eReader.

                  eReader