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Interactive motion generation from examples

Published: 01 July 2002 Publication History

Abstract

There are many applications that demand large quantities of natural looking motion. It is difficult to synthesize motion that looks natural, particularly when it is people who must move. In this paper, we present a framework that generates human motions by cutting and pasting motion capture data. Selecting a collection of clips that yields an acceptable motion is a combinatorial problem that we manage as a randomized search of a hierarchy of graphs. This approach can generate motion sequences that satisfy a variety of constraints automatically. The motions are smooth and human-looking. They are generated in real time so that we can author complex motions interactively. The algorithm generates multiple motions that satisfy a given set of constraints, allowing a variety of choices for the animator. It can easily synthesize multiple motions that interact with each other using constraints. This framework allows the extensive re-use of motion capture data for new purposes.

References

[1]
BISHOP, C. M. 1995. Neural Networks for Pattern Recognition. Clarendon Press, Oxford.
[2]
BOWDEN, R., 2000. Learning statistical models of human motion.
[3]
BRAND, M., AND HERTZMANN, A. 2001. Style machines. In Proceedings of SIGGRAPH 2000, 15-22.
[4]
BROGAN, D. C., METOYER, R. A., AND HODGINS, J. K. 1998. Dynamically simulated characters in virtual environments. IEEE Computer Graphics & Applications 18, 5, 58-69.
[5]
CHENNEY, S., AND FORSYTH, D. A. 2000. Sampling plausible solutions to multi-body constraint problems. In Proceedings of SIGGRAPH 2000, 219-228.
[6]
CHOI, M. G., LEE, J., AND SHIN, S. Y. 2000. A probabilistic approach to planning biped locomotion with prescribed motions. Tech. rep., Computer Science Department, KAIST.
[7]
DEMPSTER, W., AND GAUGHRAN, G. 1965. Properties of body segments based on size and weight. In American Journal of Anatomy, vol. 120, 33-54.
[8]
EFROS, A. A., AND LEUNG, T. K. 1999. Texture synthesis by non-parametric sampling. In ICCV (2), 1033-1038.
[9]
FORTNEY, V. 1983. The kinematics and kinetics of the running pattern of two-, four- and six-year-old children. In Research Quarterly for Exercise and Sport, vol. 54(2), 126-135.
[10]
FUNGE, J., TU, X., AND TERZOPOULOS, D. 1999. Cognitive modeling: Knowledge, reasoning and planning for intelligent characters. In Proceedings of SIGGRAPH 1999, 29-38.
[11]
GALATA, A., JOHNSON, N., AND HOGG, D. 2001. Learning variable length markov models of behaviour. In Computer Vision and Image Understanding (CVIU) Journal, vol. 81, 398-413.
[12]
GERSHO, A., AND GRAY, R. 1992. Vector Quantization and signal compression. Kluwer Academic Publishers.
[13]
GILKS, W., RICHARDSON, S., AND SPIEGELHALTER, D. 1996. Markov Chain Monte Carlo in Practice. Chapman and Hall.
[14]
GLEICHER, M. 1998. Retargetting motion to new characters. In Proceedings of SIGGRAPH 1998, vol. 32, 33-42.
[15]
GRZESZCZUK, R., AND TERZOPOULOS, D. 1995. Automated learning of muscle-actuated locomotion through control abstraction. In Proceedings of SIGGRAPH 1995, 63-70.
[16]
GRZESZCZUK, R., TERZOPOULOS, D., AND HINTON, G. 1998. Neuroanimator: Fast neural network emulation and control of physics based models. In Proceedings of SIGGRAPH 1998, 9-20.
[17]
HEEGER, D. J., AND BERGEN, J. R. 1995. Pyramid-Based texture analysis/synthesis. In Proceedings of SIGGRAPH 1995, 229-238.
[18]
HODGINS, J. K., AND POLLARD, N. S. 1997. Adapting simulated behaviors for new characters. In Proceedings of SIGGRAPH 1997, vol. 31, 153-162.
[19]
HODGINS, J., WOOTEN, W., BROGAN, D., AND O'BRIEN, J., 1995. Animated human athletics.
[20]
KOVAR, L., GLEICHER, M., AND PIGHIN, F. 2002. Motion graphs. In Proceedings of SIGGRAPH 2002.
[21]
LAMOURET, A., ANDVAN DE PANNE, M. 1996. Motion synthesis by example. In Eurographics Computer Animation and Simulation '96, 199-212.
[22]
LATOMBE, J. P. 1999. Motion planning: A journey of robots, molecules, digital actors, and other artifacts. In International Journal of Robotics Research, vol. 18, 1119-1128.
[23]
LEE, J., AND SHIN, S. Y. 1999. A hierarchical approach to interactive motion editing for human-likefigures. In Proceedings of SIGGRAPH 1999, 39-48.
[24]
LEE, J., CHAI, J., REITSMA, P., HODGINS, J., AND POLLARD, N. 2002. Interactive control of avatars animated with human motion data. In Proceedings of SIGGRAPH 2002.
[25]
LI, Y., WANG, T., AND SHUM, H. Y. 2002. Motion texture: A two-level statistical model for character motion synthesis. In Proceedings of SIGGRAPH 2002.
[26]
MATARIC, M. J. 2000. Getting humanoids to move and imitate. In IEEE Intelligent Systems, IEEE, 18-24.
[27]
MCMAHON, T. 1984. Muscles, Reflexes and Locomotion. PhD thesis, Princeton University Press.
[28]
MOLINA-TANCO, L., AND HILTON, A. 2000. Realistic synthesis of novel human movements from a database of motion capture examples. In Workshop on Human Motion (HUMO'00), 137-142.
[29]
NELSON, R., BROOKS, C., AND N. PIKE. 1977. Biomechanical comparison of male and female distance runners. In Annals of the NY Academy of Sciences, vol. 301, 793-807.
[30]
O'ROURKE, J. 1998. Computational Geometry in C. Cambridge University Press.
[31]
POPOVlC, Z. 1999. Motion Transformation by Physically Based Spacetime Optimization. PhD thesis, Carnegie Mellon University Department of Computer Science.
[32]
PULLEN, K., AND BREGLER, C. 2000. Animating by multi-level sampling. In Computer Animation 2000, 36-42. ISBN 0-7695-0683-6.
[33]
PULLEN, K., AND BREGLER, C. 2002. Motion capture assisted animation: Texturing and synthesis. In Proceedings of SIGGRAPH 2002.
[34]
ROSE, C., GUENTER, B., BODENHEIMER, B., AND COHEN, M. F. 1996. Efficient generation of motion transitions using spacetime constraints. In Proceedings of SIGGRAPH 1996, vol. 30, 147-154.
[35]
SCHODL, A., SZELISKI, R., SALESIN, D., AND ESSA, I. 2000. Video textures. In Proceedings of SIGGRAPH 2000, 489-498.
[36]
VEACH, E., AND GUIBAS, L. J. 1997. Metropolis light transport. In Proceedings of SIGGRAPH 1997, vol. 31, 65-76.
[37]
WITKIN, A., AND POPOVIC, Z. 1995. Motion warping. In Proceedings of SIGGRAPH 1995, 105-108.

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Published In

cover image ACM Transactions on Graphics
ACM Transactions on Graphics  Volume 21, Issue 3
July 2002
548 pages
ISSN:0730-0301
EISSN:1557-7368
DOI:10.1145/566654
Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 01 July 2002
Published in TOG Volume 21, Issue 3

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Author Tags

  1. animation with constraints
  2. clustering
  3. graph search
  4. human motion
  5. motion capture
  6. motion synthesis

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