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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.
