As commercial motion capture systems are widely used, more and more 3D motion libraries become available, reinforcing the demand for efficient indexing and retrieving methods. Usually, the user will only have a sketchy idea of which kind of motion to look for in the motion database. As a result, how to clearly describe the user’s demands is a bottleneck for motion retrieval system. This paper presented a framework that can handle this problem effectively for motion retrieval. This content-based retrieval system supports two kinds of query modes: textual query mode and query-by-example mode. In both query modes, user’s input is translated into scene description language first, which can be processed by the system efficiently. By using various kinds of qualitative features and adaptive segments of motion capture data stream, indexing and retrieval methods are carried out at the segment level rather than at the frame level, making them quite efficient. Some experimental examples are given to demonstrate the effectiveness and efficiency of the proposed algorithms.
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