Application of the Bayesian information criterion to keyframe extraction from motion capture data
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Motion capture is an increasingly popular animation technique; however data acquired by motion capture can become substantial. This makes it difficult to use motion capture data in a number of applications, such as motion editing, motion understanding, automatic motion summarization, motion thumbnail generation, or motion database search and retrieval. To overcome this limitation, we propose an automatic approach to extract keyframes from a motion capture sequence. We treat the input sequence as motion curves, and obtain the most salient parts of these curves using a new proposed metric, called 'motion saliency'. We select the curves to be analysed by a dimension reduction technique, Principal Component Analysis (PCA). We then apply frame reduction techniques to extract the most important frames as keyframes of the motion. With this approach, around 8% of the frames are selected to be keyframes for motion capture sequences. Copyright © 2010 John Wiley & Sons, Ltd. (This paper proposes an automatic approach to extract keyframes from a motion capture sequence. The method treats the input sequence as motion curves; reduces the dimension of the motion capture data using Principal Component Analysis; and obtains the most salient parts of these curves using a new multiscale metric, called 'motion saliency'.)