With the explosive growth of motion capture data, it becomes very imperative in animation production to have an efficient search engine to retrieve motions from large motion repository. However because of the high dimension of data space and complexity of matching methods, most of existing approaches cannot return the result in real time. This paper proposes a high level semantic feature in a low dimensional space to represent the essential characteristic of different motion classes. Based on the statistic training of Gauss Mixture Model, this feature can effectively achieve motion matching on both global clip level and local frame level. Experiment results show our approach can retrieve similar motions with rankings from large motion database in real-time, and also can make motion annotation automatically on the fly.
On the basis of the statistic training of Gauss Mixture Model, this feature can effectively achieve motion matching on both global clip level and local frame level REF .
A semantic feature for human motion retrieval
Journal of Visualization and Computer Animation
Journal of Visualization and Computer Animation
Computer Science