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Entropy‐based motion extraction for motion capture animation
Author(s) -
So Clifford K. F.,
Baciu George
Publication year - 2005
Publication title -
computer animation and virtual worlds
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.225
H-Index - 49
eISSN - 1546-427X
pISSN - 1546-4261
DOI - 10.1002/cav.107
Subject(s) - computer science , computer vision , artificial intelligence , mutual information , animation , motion capture , motion (physics) , entropy (arrow of time) , histogram , segmentation , structure from motion , motion estimation , quarter pixel motion , pattern recognition (psychology) , computer graphics (images) , image (mathematics) , physics , quantum mechanics
In this paper, we present a new segmentation solution for extracting motion patterns from motion capture data by searching for critical keyposes in the motion sequence. A rank is established for critical keyposes that identifies the significance of the directional change in motion data. The method is based on entropy metrics, specifically the mutual information measure. Displacement histograms between frames are evaluated and the mutual information metric is employed in order to calculate the inter‐frame dependency. The most significant keypose identifies the largest directional change in the motion data. This will have the lowest mutual information level from all the candidate keyposes. Less significant keyposes are then listed with higher mutual information levels. The results show that the method has higher sensitivity in the directional change than methods based on the magnitude of the velocity alone. This method is intended to provide a summary of a motion clip by ranked keyposes, which is highly useful in motion browsing and motion retrieve database system. Copyright © 2005 John Wiley & Sons, Ltd.