Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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  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,  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 beten the keyframes.  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.
['Atsushi Nakazawa', "Shin'ichiro Nakaoka", 'Katsushi Ikeuchi']
I am writing a research paper on a idea Matching and blending human motions temporal scaleable dynamic programming
[{"from": "human", "value": "I am writing a research paper on a idea Matching and blending human motions temporal scaleable dynamic programming"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  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  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,  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 beten the keyframes.  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."}]