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Validating retargeted and interpolated locomotions by dynamics-based analysis

Published:29 November 2006Publication History

ABSTRACT

Retargeting and interpolation methods may introduce physical inaccuracies in virtual human animation. This paper presents a method for evaluating the dynamical correctness of retargeted and interpolated motions. We determine resulting forces and torques at joints, with special attention to the ground reaction forces. With this intention, we propose an automatic creation of the biomechanical model of the character upgraded with the masses and inertias of the limbs and the motion mapping on this model. Then using support phase recognition, we compute resulting forces and torques by an inverse dynamics method.We evaluate how the retargeting and the interpolation methods change the physics of the motions by using the results of our analysis on artificial and real motions and using literature and experimental data from force plates. Our evaluation relies on the study of several retargeting and interpolation parameters such as the global size of the character, the relative ratios of limbs, the structure of the model, the length of step, the motion style and the character velocity.

References

  1. Abe, Y., Liu, C., and Popović, Z. 2004. Momemtum-based parametrization of dynamic character motion. In ACM Siggraph / Eurographics Symposium on Computer Animation, 173--182.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. Apkarian, J., Naumann, S., and Caims, B. 1989. A three-dimensional kinematic and dynamic model of lower limb. Journal of Biomechanics 22, 143--155.]]Google ScholarGoogle ScholarCross RefCross Ref
  3. Arikan, O., Forsyth, D., and O'Brien, J. 2005. Pushing people around. In ACM Siggraph/ Eurographics Symposium on Computer Animation, 59--67.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  4. Bindiganavale, R., and Badler, N. 1998. Motion abstraction and mapping with spatial constraints. Lecture Notes in Computer Science 1537, 70--82.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Boulic, R., and Thalmann, D. 1992. Combined direct and inverse kinematic control for articulated figure motion editing. Computer Graphics Forum 11(4), 189--202.]]Google ScholarGoogle ScholarCross RefCross Ref
  6. Choi, K., and Ko, H. 2000. On-line motion retargetting. The Journal of Visualisation and Computer Animation 11(5) (December), 223--235.]]Google ScholarGoogle ScholarCross RefCross Ref
  7. Deleva, P. 1996. Adjustments to zatsiorsky-seluyanov's segment inertia parameters. Journal of Biomechanics 29 (9) (September), 1223--1230.]]Google ScholarGoogle ScholarCross RefCross Ref
  8. Fang, A., and Pollard, N. 2003. Efficient synthesis of physically valid human motion. ACM Transactions on Graphics 22 (3) (July), 417--426.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  9. Faure, F., Debunne, G., Cani, M., and Multon, F. 1997. Dynamic analysis of human walking. In Eurographics Workshop on Computer Animation and Simulation (EGCAS).]]Google ScholarGoogle Scholar
  10. Gleicher, M., and Litwinowicz, P. 1998. Constraint-based motion adaptation. The Journal of Visualization and Computer Animation 9, 2, 65--94.]]Google ScholarGoogle ScholarCross RefCross Ref
  11. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  12. Hartenberg, R., and Denavit, J. 1955. A kinematic notation for lower-pair mechanisms based on matrices. Journal of Applied Mechanics (June), 215--221.]]Google ScholarGoogle Scholar
  13. Hodgins, J. 1998. Animating human motion. Scientific American 278(3) (March), 64--69.]]Google ScholarGoogle ScholarCross RefCross Ref
  14. Ikemoto, L., Arikan, O., and Forsyth, D. 2006. Knowing when to put your foot down. In Symposium on Interactive 3D Graphics and Games (I3D).]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  15. Khalil, W., and Kleinfinger, J. 1986. A new geometric notation for open and closed loop robots. In ICRA'86, 75--79.]]Google ScholarGoogle Scholar
  16. Kirk, A., O'Brien, J., and Forsyth, D. 2005. Skeletal parameter estimation from optical motion capture data. Computer Vision and Pattern Recognition 2005 2 (20--25 June), 782--788.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. Ko, H., and Badler, N. 1996. Animating human locomotion with inverse dynamics. IEEE Computer Graphics and Applications 16(2), 50--5.]]Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. Kovar, L., and Gleicher, M. 2004. Automated extraction and parametrization of motions in large data sets. ACM Transactions on Graphics 23 (3) (August), 559--568.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  19. Kovar, L., Gleicher, M., and Pighin, F. 2002. Motion graphs. In Proceedings of SIGGRAPH'02.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  20. Liu, C., and Popović, Z. 2002. Synthesis of complex dynamic character motion from simple animation. ACM Transactions on Graphics 21 (3) (July), 408--416.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. Liu, C., Hertzmann, A., and Popović, Z. 2005. Learning physics-based motion style with nonlinear inverse optimization. ACM Transactions on Graphics 24 (3) (July), 1071--1081.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  22. O'Brien, J., Bodenheimer, R., Brostow, G., and Hodgins, J. 2000. Automatic joint parameter estimation from magnetic capture data. In Proceedings of Graphics Interface'00, 53--60.]]Google ScholarGoogle Scholar
  23. Oore, S., Terzopoulos, D., and Hinton, G. 2002. Local physical models for interactive character animation. Computer Graphics Forum/EG2002 Proceedings 21 (3).]]Google ScholarGoogle Scholar
  24. Oshita, M., and Makinouchi, A. 2001. A dynamic motion control technique for human-like articulated figures. Computer Graphics Forum / EG2001 Proceedings 20 (3), 192--202.]]Google ScholarGoogle Scholar
  25. Park, S., Shin, H., and Shin, S. 2002. On-line locomotion generation based on motion blending. In ACM Siggraph / Eurographics Symposium on Computer Animation, 105--111.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. Popović, Z., and Witkin, A. 1999. Physically based motion transformation. In Proceedings of SIGGRAPH'99, 11--20.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  27. Pronost, N., Dumont, G., Berillon, G., and Nicolas, G. 2006. Morphological and stance interpolations in database for simulating bipedalism of virtual humans. The Visual Computer 22, 1 (January), 4--13.]]Google ScholarGoogle ScholarCross RefCross Ref
  28. Reitsma, P., and Pollard, N. 2003. Perceptual metrics for character animation: Sensitivity to errors in ballistic motion. ACM Transactions on Graphics - SIGGRAPH 2003 Proceedings 22(3), 537--542.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  29. Ren, L., Patrick, A., Efros, A., Hodgins, J., and Rehg, J. 2005. A data-driven approach to quantifying natural human motion. ACM Transactions on Graphics (SIGGRAPH 2005) 24(3), 1090--1097.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  30. Safonova, A., and Hodgins, J. 2005. Analyzing the physical correctness of interpolated human motion. In ACM Siggraph / Eurographics Symposium on Computer Animation, 171--180.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  31. Shin, H., Lee, J., Gleicher, M., and Shin, S. 2001. Computer puppetry: An importance-based approach. ACM Transactions of Graphic 20(2), 67--94.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. Silaghi, M., Plankers, R., Boulic, R., Fua, P., and Thalmann, D. 1999. Local and global skeleton fitting techniques for optical motion capture. CAPTECH'98: Modeling and Motion Capture for Virtual Environments 1537 (November), 26--40.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. Tak, S., and Ko, H. 2005. A physically-based motion retargeting filter. ACM Transaction on Graphics 24(1), 98--117.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  34. Tak, S., Song, O., and Ko, H. 2000. Motion balance filtering. Computer Graphics Forum 19(3), 437--446.]]Google ScholarGoogle ScholarCross RefCross Ref
  35. Van De Panne, M., Fiume, E., and Vranesic, Z. 1992. Control techniques for physically-based animation. In Proceedings of the Third Eurographics Workshop on Animation and Simulation.]]Google ScholarGoogle Scholar
  36. Vaughan, C., Davis, B., and O'Connor, J. 1999. Dynamics of Human Gait (2nd edition). Kiboho Publisher.]]Google ScholarGoogle Scholar
  37. Yamane, K., and Nakamura, Y. 2003. Dynamics filter - concept and implementation of on-line motion generator for human figures. IEEE Transactions on Robotics and Automation 19(3), 421--432.]]Google ScholarGoogle ScholarCross RefCross Ref
  38. Zhao, J., and Badler, N. 1994. Inverse kinematics positioning using nonlinear programming for highly articulated figures. ACM Transactions on Graphics 13 (4) (October), 313--336.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  39. Zordan, V., and Hodgins, J. 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 ScholarGoogle ScholarDigital LibraryDigital Library
  40. Zordan, V., and Van Der Horst, C. 2003. Mapping optical motion capture data to skeletal motion using a physical model. In ACM Siggraph / Eurographics Symposium on Computer Animation, 245--250.]] Google ScholarGoogle ScholarDigital LibraryDigital Library
  41. Zordan, V, Majkowska, A., Chiu, B., and Fast, M. 2005. Dynamic response for motion capture animation. ACM Transactions on Graphics 24 (3) (July), 697--701.]] Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Conferences
      GRAPHITE '06: Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia
      November 2006
      489 pages
      ISBN:1595935649
      DOI:10.1145/1174429

      Copyright © 2006 ACM

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      Publication History

      • Published: 29 November 2006

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      GRAPHITE '06 Paper Acceptance Rate47of83submissions,57%Overall Acceptance Rate124of241submissions,51%

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