Representation of Motion Spaces Using Spline Functions and Fourier Series

  • Thomas Kronfeld
  • Jens Fankhänel
  • Guido Brunnett
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8177)


Natural looking human motion are difficult to create and to manipulate because of the high dimensionality of motion data. In the last years, large collections of motion capture data are used to increase the realism in character animation. In order to simplify the generation of motion, we present a mathematical method to create variations in motion data. Given a few samples of motion data of a particular activity, our framework generates a high dimensional continuous motion space. Therewith our motion synthesis framework is able to synthesize motion by varying boundary conditions. Furthermore, we investigate the different properties of spline functions and Fourier series and their suitability for the description of complex human motion. We have derived an optimization heuristic, which is used to automatically generate the initial motion space. We have evaluated our system by comparison against ground-truth motion data and alternative methods.


Motion Data Motion Signal Motion Sequence Spline Approximation Virtual Human 
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 2014

Authors and Affiliations

  • Thomas Kronfeld
    • 1
  • Jens Fankhänel
    • 1
  • Guido Brunnett
    • 1
  1. 1.Computer Graphics LabTechnische Universität ChemnitzGermany

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