Perceptually consistent example-based human motion retrieval

Large amount of human motion capture data have been increasingly recorded and used in animation and gaming applications. Efficient retrieval of logically similar motions from a large data repository thereby serves as a fundamental basis for these motion data based applications. In this paper we present a perceptually consistent, example-based human motion retrieval approach that is capable of efficiently searching for and ranking similar motion sequences given a query motion input. Our method employs a motion pattern discovery and matching scheme that breaks human motions into a part-based, hierarchical motion representation. Building upon this representation, a fast string match algorithm is used for efficient runtime motion query processing. Finally, we conducted comparative user studies to evaluate the accuracy and perceptual-consistency of our approach by comparing it with the state of the art example-based human motion search algorithms.

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