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Motion Synthesis on Manifolds in High Dimensional Space

A lot of work has been done on how to reuse motion capture data and minimize the amount of manual work required in obtaining it. Processing and analyzing the input data, which is of a high dimensionality, recognizing the data and generating new motion behavior can be very complicated. Therefore it is necessary to reduce the data dimensions, while preserving the motion features embedded in the data. Manifold learning is the process of estimating this low dimensional structure. The projection algorithm used in this work is by Amenta and Kil [2004]. This projection procedure projects all points on a lower dimensional manifold, but in higher dimensional space thus preserving the features among the input points. This work presents 2 sample applications that can utilize this method. The first application synthesizes new motions from the input database of motions. The second application produces new intermediate motions through blending the manifolds produced in the projection procedure.