Human motion capture data segmentation based on graph partition

For better reuse of motion capture data, long motion sequences need to be segmented into multiple motion clips of simple motion types. In this paper, we propose a method for motion capture data segmentation based on graph partition. Each frame of motion sequence is viewed as a node in an undirected weighted graph, and the weight of an edge is the similarity between two frames corresponding to the two nodes connected by the edge. The optimal segmentation is obtained through graph partition algorithm, which makes the similarities of nodes in each subgraph being high, and the similarities between different subgraphs being low. After the segment scores at each frame are calculated, double thresholds decision method is conducted on the score curve to detect segment points. Experimental results show that our method obtains good segmentation results.