Abstract
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.
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Kronfeld, T., Fankhänel, J., Brunnett, G. (2014). Representation of Motion Spaces Using Spline Functions and Fourier Series. In: Floater, M., Lyche, T., Mazure, ML., Mørken, K., Schumaker, L.L. (eds) Mathematical Methods for Curves and Surfaces. MMCS 2012. Lecture Notes in Computer Science, vol 8177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54382-1_16
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DOI: https://doi.org/10.1007/978-3-642-54382-1_16
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