Tang, J. K. T., Leung, H., Komura, T., and Shum, H. P. H. Emulating human perception of motion similarity. Comput. Animat. Virtual Worlds 19, 3-4 (2008), 211–221. [PDF]
Evaluating the similarity of motions is useful for motion retrieval, motion blending, and performance analysis of dancers and athletes. Euclidean distance between corresponding joints has been widely adopted in measuring similarity of postures and hence motions. However, such a measure does not necessarily conform to the human perception of motion similarity.
In this paper, the authors propose a new similarity measure based on machine learning techniques. They make use of the results of questionnaires from subjects answering whether arbitrary pairs of motions appear similar or not. Using the relative distance between the joints as the basic features, they train the system to compute the similarity of arbitrary pair of motions. Experimental results show that our method outperforms methods based on Euclidean distance between corresponding joints.
Their method is applicable to content-based motion retrieval of human motion for large-scale database systems. It is also applicable to e-Learning systems which automatically evaluates the performance of dancers and athletes by comparing the subjects’ motions with those by experts.