Evaluating distance metrics for animation blending
Proceedings of the 4th International Conference on Foundations of Digital Games
A group of novel approaches and a toolkit for motion capture data reusing
Multimedia Tools and Applications
Pattern Recognition Letters
Generalized Model-Based Human Motion Recognition with Body Partition Index Maps
Computer Graphics Forum
Interaction Retrieval by Spacetime Proximity Graphs
Computer Graphics Forum
CUBOD: a customized body gesture design tool for end users
BCS-HCI '13 Proceedings of the 27th International BCS Human Computer Interaction Conference
| Hi-index | 0.00 |
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, we propose a new similarity measure based on machine learning techniques. We 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, we 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. Our 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. Copyright © 2008 John Wiley & Sons, Ltd.