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Journal Article

Multi-Mode Tensor Representation of Motion Data

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http://pubman.mpdl.mpg.de/cone/persons/resource/persons45076

Müller,  Meinard
Computer Graphics, MPI for Informatics, Max Planck Society;

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Citation

Krüger, B., Tautges, J., Müller, M., & Weber, A. (2008). Multi-Mode Tensor Representation of Motion Data. Journal of Virtual Reality and Broadcasting, 5(5), 1-13. Retrieved from http://www.jvrb.org/5.2008/1309/520083.pdf.


Cite as: http://hdl.handle.net/11858/00-001M-0000-000F-1C53-4
Abstract
In this paper, we investigate how a multilinear model can be used to represent human motion data. Based on technical modes (referring to degrees of freedom and number of frames) and natural modes that typically appear in the context of a motion capture session (referring to actor, style, and repetition), the motion data is encoded in form of a high-order tensor. This tensor is then reduced by using N-mode singular value decomposition. Our experiments show that the reduced model approximates the original motion better then previously introduced PCA-based approaches. Furthermore, we discuss how the tensor representation may be used as a valuable tool for the synthesis of new motions.