Abstract
This paper presents a new method to generate arm gestures which reproduces the dynamical properties of human movements. We describe a model of synergy, defined as a coordinative structure responsible for the flexible organization of joints over time when performing a movement. We propose a generic method which incorporates this synergy model into a motion controller system based on any iterative inverse kinematics technique. We show that this method is independent of the task and can be parametrized to suit an individual using a novel learning algorithm based on a motion capture database. The method yields different models of synergies for reaching tasks that are confronted to the same set of example motions. The quantitative results obtained allow us to select the best model of synergies for reaching movements and prove that our method is independent of the inverse kinematic technique used for the motion controller.
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References
Bernstein, N.A.: The co-ordination and regulation of movements. Pergamon Press, Oxford (1967)
Latash, M.L.: Synergy. Oxford University Press, Oxford (2008)
Brand, M., Hertzmann, A.: Style machines. In: SIGGRAPH 2000: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, pp. 183–192. ACM Press/Addison-Wesley Publishing Co. (2000)
Li, Y., Wang, T., Shum, H.-Y.: Motion texture: a two-level statistical model for character motion synthesis. In: SIGGRAPH 2002: Proceedings of the 29th annual conference on Computer graphics and interactive techniques, pp. 465–472. ACM, New York (2002)
Gibet, S., Marteau, P.F.: Expressive gesture animation based on non parametric learning of sensory-motor models. In: Computer Animation and Social Agents, pp. 465–472. ACM, New York (2003)
Rose, C.F., Sloan, P.-P.J., Cohen, M.F.: Artist-directed inverse-kinematics using radial basis function interpolation. Computer Graphics Forum 20, 239–250 (2001)
Grochow, K., Martin, S.L., Hertzmann, A., Popović, Z.: Style-based inverse kinematics. ACM Trans. Graph. 23(3), 522–531 (2004)
Wang, Y., Liu, Z., Zhou, L.: Key-styling: learning motion style for real-time synthesis of 3d animation. Computer Animation and Virtual Worlds 17(3-4), 229–237 (2006)
Ong, E.-J., Hilton, A.: Learnt inverse kinematics for animation synthesis. Graphical Models 68(5-6), 472–483 (2006)
Liu, C.K., Hertzmann, A., Popović, Z.: Learning physics-based motion style with nonlinear inverse optimization. ACM Trans. Graph. 24(3), 1071–1081 (2005)
Oyama, E., Agah, A., MacDorman, K.F., Maeda, T., Tachi, S.: A modular neural network architecture for inverse kinematics model learning. Neurocomputing 38-40, 797–805 (2001)
Chai, J., Hodgins, J.K.: Constraint-based motion optimization using a statistical dynamic model. In: SIGGRAPH 2007: ACM SIGGRAPH 2007 papers, p. 8. ACM, New York (2007)
Baerlocher, P., Boulic, R.: An inverse kinematic architecture enforcing an arbitrary number of strict priority levels. The Visual Computer 20(6), 402–417 (2004)
Le Callennec, B., Boulic, R.: Interactive motion deformation with prioritized constraints. Graphical Models 68(2), 175–193 (2006)
Yang, F., Ding, L., Yang, C., Yuan, X.: An algorithm for simulating human arm movement considering the comfort level. Simulation Modelling Practice and Theory 13(5), 437–449 (2005)
Komura, T., Kuroda, A., Kudoh, S., Tai, C.-L., Shinagawa, Y.: An inverse kinematics method for 3d figures with motion data. In: Computer Graphics International, pp. 266–271 (2003)
Raunhardt, D., Boulic, R.: Motion constraint. The Visual Computer 25(5), 509–518 (2009)
Tournier, M., Wu, X., Courty, N., Arnaud, E., Reveret, L.: Motion compression using principal geodesics analysis. Eurographics 28(2) (April 2009) (to appear)
Gibet, S., Marteau, P.F.: A self-organized model for the control, planning and learning of nonlinear multi-dimensional systems using a sensory feedback. Applied Intelligence 4(4), 337–349 (1994)
Buss, S.R., Kim, J.-S.: Selectively damped least squares for inverse kinematics. Journal of Graphics Tools 10(3), 37–49 (2005)
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Aubry, M., Julliard, F., Gibet, S. (2010). Modeling Joint Synergies to Synthesize Realistic Movements. In: Kopp, S., Wachsmuth, I. (eds) Gesture in Embodied Communication and Human-Computer Interaction. GW 2009. Lecture Notes in Computer Science(), vol 5934. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12553-9_20
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DOI: https://doi.org/10.1007/978-3-642-12553-9_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-12552-2
Online ISBN: 978-3-642-12553-9
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