Automated extraction and parameterization of motions in large data sets
ACM SIGGRAPH 2004 Papers
Action synopsis: pose selection and illustration
ACM SIGGRAPH 2005 Papers
Efficient content-based retrieval of motion capture data
ACM SIGGRAPH 2005 Papers
An efficient search algorithm for motion data using weighted PCA
Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation
Motion templates for automatic classification and retrieval of motion capture data
Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation
Indexing large human-motion databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Information Retrieval for Music and Motion
Information Retrieval for Music and Motion
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)
Human Motion: Understanding, Modelling, Capture, and Animation (Computational Imaging and Vision)
Efficient and robust annotation of motion capture data
Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation
Pattern Recognition Letters
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In the last years, various algorithms have been proposed for automatic classification and retrieval of motion capture data. Here, one main difficulty is due to the fact that similar types of motions may exhibit significant spatial as well as temporal variations. To cope with such variations, previous algorithms often rely on warping and alignment techniques that are computationally time and cost intensive. In this paper, we present a novel keyframe-based algorithm that significantly speeds up the retrieval process and drastically reduces memory requirements. In contrast to previous index-based strategies, our recursive algorithm can cope with temporal variations. In particular, the degree of admissible deformation tolerance between the queried keyframes can be controlled by an explicit stiffness parameter. While our algorithm works for general multimedia data, we concentrate on demonstrating the practicability of our concept by means of the motion retrieval scenario. Our experiments show that one can typically cut down the search space from several hours to a couple of minutes of motion capture data within a fraction of a second.