Matching and blending human motions temporal scaleable dynamic programming

This paper presents a method for matching the frames of the human motions acquired by a motion capture system, and then creating blended (interpolated) motions according to the matching result. This matching method is basically a variation of a dynamic programming (DP) matching but we enhanced it to enable it to detect the timescale parameters. This scaleable dynamic programming (scaleable-DP) can match and evaluate the same class of motions such as walking, running, stepping and their timescale parameters. This approach is adaptable for differences in individuals, such as body sizes and timing of the stop-frames. On the blending pipeline, we first generate the keyframes according to the matching result. The keyframes are generated by considering the spatial and temporal difference of individual motions. After that, transition motions are synthesized between the keyframes. We experimented with our approach by using 15 gait motions and 5 dance motions. The results of these demonstrations show the validity of the proposed algorithm.