Abstract
The synthesis of realistic complex body movements in real-time is a difficult problem in computer graphics and in robotics. High realism requires the accurate modeling of the details of the trajectories for a large number of degrees of freedom. At the same time, real-time animation necessitates flexible systems that can react in an online fashion, adapting to external constraints. Such online systems are suitable for the self-organization of complex behavior by the dynamic interaction between multiple autonomous characters in the scene. In this paper we present a novel approach for the online synthesis of realistic human body movements. The proposed model is inspired by concepts from motor control. It approximates movements by superposition of movement primitives (synergies) that are learned from motion capture data applying a new blind source separation algorithm. The learned generative model can synthesize periodic and non-periodic movements, achieving high degrees of realism with a very small number of synergies. For obtaining a system that is suitable for real-time synthesis, the primitives are approximated by the solutions of low-dimensional nonlinear dynamical systems (dynamic primitives). The application of a new type of stability analysis (contraction theory) permits the design of complex networks of such dynamic primitives, resulting in a stable overall system architecture. We discuss a number of applications of this framework and demonstrate that it is suitable for the self-organization of complex behaviors, such as navigation, synchronized crowd behavior and dancing.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Rose, C., Cohen, M., Bodenheimer, B.: Verbs and adverbs: Multidimensional motion interpolation. IEEE Computer Graphics and Applications 18(5), 32–40 (1998)
Witkin, A., Popovi\(\acute{c}\), Z.: Motion warping. In: Proc. ACM SIGGRAPH 1995, vol. 29, pp. 105–108 (1995)
Gleicher, M.: Retargetting motion to new characters. In: Proc. ACM SIGGRAPH 1998, pp. 33–42 (1998)
Gleicher, M., Shin, H.J., Kovar, L., Jepsen, A.: Snap-together motion: Assembling run-time animation. ACM Trans. on Graphics, SIGGRAPH 2003 22(3), 702 (2003)
Arikan, O., Forsyth, D.A., O’Brien, J.F.: Motion synthesis from annotations. ACM Trans. on Graphics, SIGGRAPH 2003 22(3), 402–408 (2003)
Grzeszczuk, R., Terzopoulos, D., Hinton, G.: Neuroanimator: Fast neural network emulation and control of physics based models. In: Int. Conf. on Comp. Graph. and Interactive Techniques, Proc. ACM SIGGRAPH 1998, pp. 9–20 (1998)
Shao, W., Terzopoulos, D.: Artificial intelligence for animation: Autonomous pedestrians. Proc. ACM SIGGRAPH 2005 69(5-6), 19–28 (2005)
Hsu, E., Pulli, K., Popovi\(\acute{c}\), J.: Style translation for human motion. ACM Trans. on Graphics, SIGGRAPH 2005 24(3), 1082–1089 (2005)
Chai, J., Hodgins, J.: Performance animation from low-dimensional control signals. ACM Trans. on Graphics, SIGGRAPH 2005 24(3), 686–696 (2005)
Bernstein, N.A.: The coordination and regulation of movements. Pergamon Press, Oxford (1967)
Flash, T., Hochner, B.: Motor primitives in vertebrates and invertebrates. Curr. Opin. Neurobiol. 15(6), 660–666 (2005)
d’Avella, A., Bizzi, E.: Shared and specific muscle synergies in neural motor behaviours. Proc. Natl. Acad. Sci. 102(8), 3076–3081 (2005)
Ivanenko, Y., Poppele, R., Lacquaniti, F.: Five basic muscle activation patterns account for muscle activity during human locomotion. Journal of Physiology 556, 267–282 (2004)
Santello, M., Flanders, M., Soechting, J.: Postural hand synergies for tool use. Journal of Neuroscience 18(23), 10105–10115 (1998)
Safanova, A., Hodgins, J.K., Pollard, N.S.: Synthesizing physically realistic human motion in low-dimensional, behavior-specific spaces. ACM Trans. on Graphics, Proc. SIGGRAPH 2004 23(3), 514–521 (2004)
Park, A., Mukovskiy, A., Omlor, L., Giese, M.A.: Synthesis of character behaviour by dynamic interaction of synergies learned from motion capture data. In: The 16-th Int. Conf. in Central Europe on Comp. Graphics, Visualization and Computer Vision 2008, WSCG 2008, pp. 9–16 (2008)
Park, A., Mukovskiy, A., Omlor, L., Giese, M.A.: Self organized character animation based on learned synergies from full-body motion capture data. In: International Conference on Cognitive Systems, CogSys 2008 (2008)
Unuma, M., Anjyo, K., Takeuchi, R.: Fourier principles for emotion-based human figure animation. Proc. ACM SIGGRAPH 1995 22, 91–96 (1995)
Schaal, S., Ijspeert, A., Billard, A.: Computational approaches to motor learning by imitation. Philos. Trans. R. Soc. of London, Biol. Sci. 358(1431), 537–547 (2003)
Ijspeert, A., Nakanishi, J., Schaal, S.: Learning attractor landscapes for learning motor primitives. Adv. Neural Inf. Process Systems 15, 1547–1554 (2002)
Omlor, L., Giese, M.A.: Extraction of spatio-temporal primitives of emotional body expressions. Neurocomputing 70, 10–12 (2007)
Omlor, L., Giese, M.A.: Blind source separation for over-determined delayed mixtures. Adv. in Neural Inf. Process Systems 19, 1049–1056 (2006)
Schöner, G., Dose, M., Engels, C.: Dynamics of behavior: Theory and applications for autonomous robot architectures. Robotics and Autonomous Systems 16(2-4), 213–245 (1995)
Buchli, J., Righetti, L., Ijspeert, A.J.: Engineering entrainment and adaptation in limit cycle systems - from biological inspiration to applications in robotics. Biological Cybernetics 95(6), 645–664 (2006)
Fod, A., Matari\(\acute{c}\), M.J., Jenkins, O.C.: Motor primitives in vertebrates and invertebrates. Autonomous Robots 12(1), 39–54 (2002)
Soechting, J., Lacquaniti, F.: Invariant characteristics of a pointing movement in man. Journal of Neuroscience 1, 710–720 (1981)
Morasso, P.: Spatial control of arm movements. Exp. Brain Res. 42(2) (1981)
Vapnik, N.V.: Statistical Learning Theory. Wiley Interscience, Hoboken (1998)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines (2001), http://www.csie.ntu.edu.tw/~cjlin/libsvm
Wang, W., Slotine, J.J.E.: On partial contraction analysis for coupled nonlinear oscillators. Biological Cybernetics 92(1), 38–53 (2005)
Warren, W.: The dynamics of perception and action. Psychological Review 113(2), 358–389 (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Giese, M.A., Mukovskiy, A., Park, AN., Omlor, L., Slotine, JJ.E. (2009). Real-Time Synthesis of Body Movements Based on Learned Primitives. In: Cremers, D., Rosenhahn, B., Yuille, A.L., Schmidt, F.R. (eds) Statistical and Geometrical Approaches to Visual Motion Analysis. Lecture Notes in Computer Science, vol 5604. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03061-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-642-03061-1_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03060-4
Online ISBN: 978-3-642-03061-1
eBook Packages: Computer ScienceComputer Science (R0)