“Interaction capture and synthesis” by Kry and Pai

  • ©Paul G. Kry and Dinesh K. Pai

Conference:


Type:


Title:

    Interaction capture and synthesis

Presenter(s)/Author(s):



Abstract:


    Modifying motion capture to satisfy the constraints of new animation is difficult when contact is involved, and a critical problem for animation of hands. The compliance with which a character makes contact also reveals important aspects of the movement’s purpose. We present a new technique called interaction capture, for capturing these contact phenomena. We capture contact forces at the same time as motion, at a high rate, and use both to estimate a nominal reference trajectory and joint compliance. Unlike traditional methods, our method estimates joint compliance without the need for motorized perturbation devices. New interactions can then be synthesized by physically based simulation. We describe a novel position-based linear complementarity problem formulation that includes friction, breaking contact, and the compliant coupling between contacts at different fingers. The technique is validated using data from previous work and our own perturbation-based estimates.

References:


    1. Anitescu, M., and Potra, F. A. 2002. A time-stepping method for stiff multibody dynamics with contact and friction. International J. Numer. Methods Engineering 55, 753–784.Google ScholarCross Ref
    2. Baraff, D. 1994. Fast contact force computation for nonpenetrating rigid bodies. In Proceedings of SIGGRAPH ’94, ACM Press, A. Glassner, Ed., Computer Graphics Proceedings, Annual Conference Series, 23–34. Google ScholarDigital Library
    3. Bizzi, E., Hogan, N., Mussa-Ivaldi, F. A., and Giszter, A. 1992. Does the nervous system use equilibrium-point control to guide single and multiple joint movements? Behavioral and Brain Sciences 15, 603–613.Google ScholarCross Ref
    4. Burdet, E., Osu, R., Franklin, D. W., Milner, T. E., and Kawato, M. 2001. The central nervous system stabilizes unstable dynamics by learning optimal impedance. Nature 414 (November), 446–449.Google ScholarCross Ref
    5. Catmull, E. 1972. A system for computer generated movies. In Proceedings of the ACM Annual Conference, 422–431. Google ScholarDigital Library
    6. Delp, S., and Loan, J. 2000. A computational framework for simulating and analyzing human and animal movement. Computing in Science and Engineering (September). Google ScholarDigital Library
    7. El Koura, G., and Singh, K. 2003. Handrix: Animating the human hand. In ACM SIGGRAPH Symposium on Computer Animation, 110–119. Google ScholarDigital Library
    8. Feldman, A. G. 1986. Once more on the equilibrium-point hypothesis (lambda model) for motor control. Journal of Motor Behavior 18, 1 (March), 17–54.Google ScholarCross Ref
    9. Gleicher, M. 1998. Retargetting motion to new characters. In SIGGRAPH ’98: Proceedings of the 25th annual conference on Computer graphics and interactive techniques, 33–42. Google ScholarDigital Library
    10. Gomi, H., and Kawato, M. 1997. Human arm stiffness and equilibrium-point trajectory during multi-joint movement. Biological Cybernetics 76, 163–171.Google ScholarCross Ref
    11. Hajian, A. Z., and Howe, R. D. 1997. Identification of the mechanical impedance at the human finger tip. Journal of biomechanical engineering 119, 1, 109–114.Google ScholarCross Ref
    12. Hasser, C. J., and Cutkosky, M. R. 2002. System identificaiton of the human hand grasping a haptic knob. In Proceedings of the 10th Symposium on Haptic Interfaces for Virtual Environments and Teleoperator Systems (HAPTICS’02). Google ScholarDigital Library
    13. Hodgins, J. K., and Pollard, N. S. 1997. Adapting simulated behaviors for new characters. In SIGGRAPH ’97: Proceedings of the 24th annual conference on Computer graphics and interactive techniques, 153–162. Google ScholarDigital Library
    14. Hogan, N. 1984. Adaptive control of mechanical impedance by coactivation of antagonist muscles. IEEE Transactions on Automatic Control AC-29, 8, 681–690.Google ScholarCross Ref
    15. Ikemoto, L., Arikan, O., and Forsyth, D. A. 2006. Knowing when to put your foot down. In SI3D ’06: Proceedings of the 2006 symposium on Interactive 3D graphics and games, ACM Press, New York, NY, USA, 49–53. Google ScholarDigital Library
    16. Johansson, R. S. 1996. Sensory and memory information in the control of dextrous manipulation. Kluwer Academic, 205–260.Google Scholar
    17. Johansson, R. S. 1998. Sensory input and control of grip. In Novartis Foundation Symposium, vol. 218, 45–63.Google Scholar
    18. Kuchenbecker, K. J., Park, J. G., and Niemeyer, G. 2003. Characterizing the human wrist for improved haptic interaction. In Proc. ASME Int. Mechanical Engineering Congress and Exposition, vol. 2.Google Scholar
    19. Kurihara, T., and Miyata, N. 2004. Modeling deformable human hands from medical images. In ACM SIGGRAPH Symposium on Computer Animation, 357–365. Google ScholarDigital Library
    20. Liu, C. K., Hertzmann, A., and Popovic, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. Graph. 24, 3, 1071–1081. Google ScholarDigital Library
    21. Lotstedt, P. 1981. Coulomb friction in two-dimensional rigid-body systems. In Zeitschrift fur Angewandte Mathematik und Mechanik, 605–615.Google Scholar
    22. Miller, A. T., and Christensen, H. I. 2003. Implementation of multi-rigid-body dynamics within a robotic grasping simulator. In IEEE International Conference on Robotics and Automation, vol. 2, 2262–2268.Google Scholar
    23. Milner, T. E., and Franklin, D. W. 1998. Characterization of multijoint finger stiffness: dependence on finger posture and force direction. IEEE Transactions on Biomedical Engineering 45, 11 (November), 1363–1375.Google ScholarCross Ref
    24. Murty, K. G. 1988. Linear Complementarity, Linear and Nonlinear Programming. Heldermann Verlag, Berlin.Google Scholar
    25. Neff, M., and Fiume, E. 2002. Modeling tension and relaxation for computer animation. In SCA ’02: Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation, 81–88. Google ScholarDigital Library
    26. Pauly, M., Pai, D. K., and Guibas, L. J. 2004. Quasi-rigid objects in contact. In SCA ’04: Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ACM Press, New York, NY, USA, 109–119. Google ScholarDigital Library
    27. Pollard, N. S., and Zordan, V. B. 2005. Physically based grasping control from example. In SCA ’05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ACM Press, New York, NY, USA, 311–318. Google ScholarDigital Library
    28. Popovic, Z., and Witkin, A. 1999. Physically based motion transformation. In SIGGRAPH ’99: Proceedings of the 26th annual conference on Computer graphics and interactive techniques, ACM Press/Addison-Wesley Publishing Co., 11–20. Google ScholarDigital Library
    29. Rancourt, D., and Hogan, N. 2001. Stability in force-production tasks. Journal of Motor Behavior 33, 2, 193–204.Google ScholarCross Ref
    30. Shapiro, A., Pighin, F., and Faloutsos, P. 2003. Hybrid control for interactive character animation. In PG ’03: Proceedings of the 11th Pacific Conference on Computer Graphics and Applications, IEEE Computer Society, 455. Google ScholarDigital Library
    31. Stewart, D. E., and Trinkle, J. C. 1996. An implicit time-stepping scheme for rigid body dynamics with inelastic collisions and coulomb friction. International J. Numer. Methods Engineering 39, 2673–2691.Google ScholarCross Ref
    32. Teran, J., Blemker, S., Hing, V. N. T., and Fedkiw, R. 2003. Finite volume methods for the simulation of skeletal muscle. In SCA ’03: Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, Eurographics Association, 68–74. Google ScholarDigital Library
    33. Tsang, W., Singh, K., and Fiume, E. 2005. Helping hand: an anatomically accurate inverse dynamics solution for unconstrained hand motion. In SCA ’05: Proceedings of the 2005 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, ACM Press, New York, NY, USA, 319–328. Google ScholarDigital Library
    34. Xu, Y., and Hollerbach, J. M. 1999. A robust ensemble data method for identification of human joint mechanical properties during movement. IEEE Transactions on Biomedical Engineering 46, 4 (April), 409–419.Google Scholar
    35. Yin, K., Cline, M. B., and Pai, D. K. 2003. Motion perturbation based on simple neuromotor control models. In Proceedings of the 11th Pacific Conference on Computer Graphics and Applications, 445–449. Google ScholarDigital Library
    36. Zordan, V. B., and Hodgins, J. K. 2002. Motion capture-driven simulations that hit and react. In SCA ’02: Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation, 89–96. Google ScholarDigital Library
    37. Zordan, V. B., Majkowska, A., Chiu, B., and Fast, M. 2005. Dynamic response for motion capture animation. ACM Trans. Graph. 24, 3, 697–701. Google ScholarDigital Library


ACM Digital Library Publication: