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Real-Time Synthesis of Body Movements Based on Learned Primitives

  • Martin A. Giese
  • Albert Mukovskiy
  • Aee-Ni Park
  • Lars Omlor
  • Jean-Jacques E. Slotine
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5604)

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.

Keywords

Support Vector Regression Central Pattern Generator Movement Primitive Motion Capture Data Motor Primitive 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Martin A. Giese
    • 1
  • Albert Mukovskiy
    • 1
  • Aee-Ni Park
    • 1
  • Lars Omlor
    • 1
  • Jean-Jacques E. Slotine
    • 2
  1. 1.Section Computational Sensomotorics, Hertie Institute for Clinical Brain Research & Center for Integrative NeuroscienceUniversity of TübingenGermany
  2. 2.Nonlinear Systems LaboratoryMassachusetts Institute of TechnologyUSA

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