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Planning biped locomotion using motion capture data and probabilistic roadmaps

Publication: ACM Transactions on GraphicsApril 2003 https://doi.org/10.1145/636886.636889
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ACM Transactions on Graphics
Volume 22, Issue 2
April 2003
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Abstract

Typical high-level directives for locomotion of human-like characters are useful for interactive games and simulations as well as for off-line production animation. In this paper, we present a new scheme for planning natural-looking locomotion of a biped figure to facilitate rapid motion prototyping and task-level motion generation. Given start and goal positions in a virtual environment, our scheme gives a sequence of motions to move from the start to the goal using a set of live-captured motion clips. Based on a novel combination of probabilistic path planning and hierarchical displacement mapping, our scheme consists of three parts: roadmap construction, roadmap search, and motion generation. We randomly sample a set of valid footholds of the biped figure from the environment to construct a directed graph, called a roadmap, that guides the locomotion of the figure. Every edge of the roadmap is associated with a live-captured motion clip. Augmenting the roadmap with a posture transition graph, we traverse it to obtain the sequence of input motion clips and that of target footprints. We finally adapt the motion sequence to the constraints specified by the footprint sequence to generate a desired locomotion.

References

  1. Arikan, O. and Forsyth, D. 2002. Interactive motion generation from examples. ACM Trans. Graph. 21, 3, 483--490.]] Google ScholarGoogle Scholar
  2. Badler, N. I., Bindiganavale, R., Granieri, J. P., Wei, S., and Zhao, X. 1994. Posture interpolation with collision avoidance. In Proceedings of the Conference on Computer Animation '94. 13--20.]]Google ScholarGoogle Scholar
  3. Bandi, S. and Thalmann, D. 1998. Space discretization for efficient human navigation. Comput. Graph. Forum 17, 3, 195--206.]]Google ScholarGoogle Scholar
  4. Barraquand, J., Kavraki, L., Latombe, J.-C., Li, T. Y., Motwani, R., and Raghavan, P. 1997. A random sampling scheme for path planning. Int. J. Robotics Res. 16, 6, 759--774.]] Google ScholarGoogle Scholar
  5. Barraquand, J. and Latombe, J.-C. 1991. Robot motion planning: A distributed representation approach. Int. J. Robotics Res. 10, 6, 628--649.]] Google ScholarGoogle Scholar
  6. Boulic, R., Thalmann, N. M., and Thalmann, D. 1990. A global human walking model with real-time kinematic personification. The Visual Computer 6, 6, 344--368.]] Google ScholarGoogle Scholar
  7. Bowden, R. 2000. Learning statistical models of human motion. In Proceedings of the IEEE Workshop on Human Modeling, Analysis and Synthesis, CVPR2000.]]Google ScholarGoogle Scholar
  8. Brand, M. and Hertzmann, A. 2000. Style machines. Comput. Graph. 34, 183--192.]] Google ScholarGoogle Scholar
  9. Bruderlin, A. and Calvert, T. W. 1989. Goal-directed animation of human walking. Comput. Graph. 23, 233--942.]] Google ScholarGoogle Scholar
  10. Bruderlin, A. and Williams, L. 1995. Motion signal processing. Comput. Graph. 29, 97--104.]] Google ScholarGoogle Scholar
  11. Chung, S.-K. and Hahn, J. K. 1999. Animation of human walking in virtual environments. In Proceedings of the Conference on Computer Animation '99. 4--15.]] Google ScholarGoogle Scholar
  12. Cohen, M. F. 1992. Interactive spacetime control for animation. Comput. Graph. 26, 293--302.]] Google ScholarGoogle Scholar
  13. Dijkstra, E. W. 1959. A note on two problems in connection with graphs. Numerische Mathematik 1, 269--271.]]Google ScholarGoogle Scholar
  14. Gleicher, M. 1998. Retargetting motion to new characters. Comput. Graph. 32, 33--42.]] Google ScholarGoogle Scholar
  15. Gleicher, M. 2001. Motion path editing. In Proceedings of the ACM Symposium on Interactive 3D Graphics. 195--202.]] Google ScholarGoogle Scholar
  16. Gottschalk, S., Lin, M. C., and Manocha, D. 1996. OBBtree: A hierarchical structure for rapid interference detection. Comput. Graph. 30, 171--180.]]Google ScholarGoogle Scholar
  17. Hodgins, J. K. and Pollard, N. S. 1997. Adapting simulated behaviors for new characters. Comput. Graph. 31, 153--162.]]Google ScholarGoogle Scholar
  18. Hodgins, J. K., Wooten, W. L., Brogan, D. C., and O'Brien, J. F. 1995. Animating human athletics. Comput. Graph. 29, 75--78.]]Google ScholarGoogle Scholar
  19. Hwang, Y. and Ahuja, N. 1992. Gross motion planning---a survey. ACM Comput. Surveys 24, 3, 219--291.]] Google ScholarGoogle Scholar
  20. Kalisiak, M. and van de Panne, M. 2000. A grasp-based motion planning algorithm for character animation. In Proceedings of CAS '2000---Eurographics Workshop on Simulation and Animation. 43--58.]]Google ScholarGoogle Scholar
  21. Kavraki, L., Kolountzakis, M., and Latombe, J.-C. 1996a. Analysis of probabilistic roadmaps for path planning. In Proceedings of the IEEE International Conference on Robotics and Automation. 3020--3025.]]Google ScholarGoogle Scholar
  22. Kavraki, L., Svestka, P., Latombe, J.-C., and Overmars, M. H. 1996b. Probabilistic roadmaps for path planning in high dimensional configuration space. IEEE Trans. Robotics and Automation 12, 4, 566--580.]]Google ScholarGoogle Scholar
  23. Kavraki, L. and Latombe, J.-C. 1994. Randomized preprocessing of configuration space for fast path planning. In Proceedings of the IEEE International Conference on Robotics and Automation. 2138--2145.]]Google ScholarGoogle Scholar
  24. Kavraki, L., Latombe, J.-C., Motwani, R., and Raghavan, P. 1995. Randomized query processing in robot motion planning. In Proceedings of the 27th Annual ACM Symposium on Theory of Computing (STOC). 353--362.]] Google ScholarGoogle Scholar
  25. Kindel, R., Hsu, D., Latombe, J.-C., and Rock, S. 2000. Kinodynamic motion planning amidst moving obstacles. In Proceedings of the IEEE International Conference on Robotics and Automation. 537--543.]]Google ScholarGoogle Scholar
  26. Ko, H. and Badler, N. I. 1996. Animating human locomotion with inverse dynamics. IEEE Comput. Graph. Appl. 16, 2, 50--29.]]Google ScholarGoogle Scholar
  27. Ko, H. and Cremer, J. 1995. VRLOCO: Real-time human locomotion from positional input streams. Presence: Teleoperations and Virtual Environments 5, 4, 1--15.]]Google ScholarGoogle Scholar
  28. Koga, Y., Kondo, K., Kuffner, J., and Latombe, J.-C. 1994. Planning motions with intentions. Comput. Graph. 28, 395--408.]]Google ScholarGoogle Scholar
  29. Korein, J. U. and Badler, N. I. 1982. Techniques for generating the goal-directed motion of articulated structures. IEEE Comput. Graph. Automation 2, 9, 71--81.]]Google ScholarGoogle Scholar
  30. Kovar, L., Gleicher, M., and Pighin, F. 2002. Motion graphs. ACM Trans. Graph. 21, 3, 473--482.]] Google ScholarGoogle Scholar
  31. Kuffner, J. and Latombe, J.-C. 1999. Fast synthetic vision, memory, and learning for virtual humans. In Proceedings of the Conference on Computer Animation '99. 118--127.]] Google ScholarGoogle Scholar
  32. Lamouret, A. and van de Panne, M. 1996. Motion synthesis by example. In Proceedings of CAS '96---Eurographics Workshop on Simulation and Animation. 199--212.]] Google ScholarGoogle Scholar
  33. Laszlo, J., van de Panne, M., and Fiume, E. 1996. Limit cycle control and its application to the animation of balancing and walking. Comput. Graph. 30, 155--162.]]Google ScholarGoogle Scholar
  34. Latombe, J.-C. 1991. Robot Motion Planning. Kluwer Academic Publishers.]] Google ScholarGoogle Scholar
  35. Lee, J., Chai, J., Reitsma, P., Hodgins, J. K., and Pollard, N. 2002. Interactive control of avartars animated with human motion data. ACM Trans. Graph. 21, 3, 491--500.]] Google ScholarGoogle Scholar
  36. Lee, J. and Shin, S. Y. 1999. A hierarchical approach to interactive motion editing for human-like figures. Comput. Graph. 33, 395--408.]]Google ScholarGoogle Scholar
  37. Lee, J. and Shin, S. Y. 2001. A coordinate-invariant approach to multiresolution motion analysis. Graph. Models 63, 2, 87--105.]] Google ScholarGoogle Scholar
  38. Lee, J. and Shin, S. Y. 2002. General construction of time-domain filters for orientation data. IEEE Trans. Visual. Comput. Graph. 8, 2, 119--128.]] Google ScholarGoogle Scholar
  39. Li, Y., Wang, T., and Shum, H.-Y. 2002. Motion texture: A two-level statistical model for character motion synthesis. ACM Trans. Graph. 21, 3, 465--472.]] Google ScholarGoogle Scholar
  40. Lin, M. C. and Manocha, D. 1995. Fast interference detection between geometric models. Vis. Comput. 11, 10, 542--561.]]Google ScholarGoogle Scholar
  41. Marti, J. and Bunn, C. 1994. Automated path planning for simulation. In Proceedings of the Conference on AI, Simulation and Planning, AIS94.]]Google ScholarGoogle Scholar
  42. Mirtich, B. and Canny, J. F. 1995. Impulse-based simulation of rigid bodies. In Proceedings of the ACM Symposium on Interactive 3D Graphics. 181--188.]] Google ScholarGoogle Scholar
  43. Molina-Tanco, L. and Hilton, A. 2000. Realistic synthesis of novel human movements from a database of motion capture examples. In Proceedings of the IEEE Workshop on Human Motion. 137--142.]] Google ScholarGoogle Scholar
  44. Multon, F., France, L., Cani, M.-P., and Debunne, G. 1999. Computer animation of human walking: a survey. J. Vis. Comput. Animation 10, 3, 39--54.]]Google ScholarGoogle Scholar
  45. Noser, H., Pandzic, I. S., Capin, T. K., Thalmann, N. M., and Thalmann, D. 1996. Playing games through the virtual life network. In Proceedings of the Conference on Alife '96.]]Google ScholarGoogle Scholar
  46. Noser, H., Renault, O., Thalmann, D., and Thalmann, N. M. 1995. Navigation for digital actors based on synthetic vision, memory, and learning. Comput. Graph. 19, 1, 7--19.]]Google ScholarGoogle Scholar
  47. Overmars, M. H. and Svestka, P. 1994. A probabilistic learning approach to motion planning. In Proceedings of the Workshop on Algorithmic Foundations of Robotics. 19--37.]] Google ScholarGoogle Scholar
  48. Preparata, F. P. and Shamos, M. I. 1985. Computational Geometry: An Introduction. Springer-Verlag.]] Google ScholarGoogle Scholar
  49. Pullen, K. and Bregler, C. 2002. Motion capture assisted animation: Texturing and synthesis. ACM Trans. Graph. 21, 3, 501--508.]] Google ScholarGoogle Scholar
  50. Raibert, M. H. and Hodgins, J. K. 1991. Animation of dynamic legged locomotion. Comput. Graph. 25, 319--358.]] Google ScholarGoogle Scholar
  51. Reich, B. D., Ko, H., Becket, W., and Badler, N. I. 1994. Terrain reasoning for human locomotion. In Proceedings of the Conference on Computer Animation '94. 77--82.]]Google ScholarGoogle Scholar
  52. Reynolds, C. W. 1987. Flocks, herds, and schools: A distributed behavioral model. Comput. Graph. 21, 25--34.]] Google ScholarGoogle Scholar
  53. Rose, C., Cohen, M. F., and Bodenheimer, B. 1998. Verbs and adverbs: Multidimensional motion interpolation. IEEE Comput. Graph. Appl. 18, 5, 32--40.]] Google ScholarGoogle Scholar
  54. Rose, C., Guenter, B., Bodenheimer, B., and Cohen, M. F. 1996. Efficient generation of motion transitions using spacetime constraints. Comput. Graph. 30, 147--154.]]Google ScholarGoogle Scholar
  55. Schödl, A., Szeliski, R., Salesin, D. H., and Essa, I. 2000. Video textures. Comput. Graph. 34, 489--498.]] Google ScholarGoogle Scholar
  56. Shoemake, K. 1985. Animating rotation with quaternion curves. Comput. Graph. 19, 245--54.]] Google ScholarGoogle Scholar
  57. Sun, H. C. and Metaxas, D. N. 2001. Automating gait generation. Comput. Graph. 35, 261--269.]]Google ScholarGoogle Scholar
  58. Svestka, P. and Overmars, M. H. 1998. Coordinated path planning for multiple robots. Robotics and Autonomous Systems 23, 4, 125--152.]]Google ScholarGoogle Scholar
  59. Torkos, N. and van de Panne, M. 1998. Footprint-based quadruped motion synthesis. In Proceedings of Graphics Interface '98. 151--160.]]Google ScholarGoogle Scholar
  60. Tu, X. and Terzopoulos, D. 1994. Artificial fishes: Physics, locomotion, perception, behavior. Comput. Graph. 28, 43--50.]]Google ScholarGoogle Scholar
  61. Unuma, M., Anjyo, K., and Takeuchi, R. 1995. Fourier principles for emotion-based human figure animation. Comput. Graph. 29, 91--96.]]Google ScholarGoogle Scholar
  62. van de Panne, M. 1997. From footprints to animation. Comput. Graph. Forum 16, 4, 211--223.]]Google ScholarGoogle Scholar
  63. Witkin, A. and Popović, Z. 1995. Motion warping. Comput. Graph. 29, 105--108.]]Google ScholarGoogle Scholar

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  1. Planning biped locomotion using motion capture data and probabilistic roadmaps

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