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A semantic feature for human motion retrieval.

Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X. and Zhang, J. J., 2013. A semantic feature for human motion retrieval. Computer Animation and Virtual Worlds, 24 (3-4), 399 - 407 .

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DOI: 10.1002/cav.1505


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 the 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. 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. Experiment results show that 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. Copyright © 2013 John Wiley & Sons, Ltd.

Item Type:Article
Group:Faculty of Media & Communication
ID Code:21412
Deposited By: Symplectic RT2
Deposited On:10 Sep 2014 15:25
Last Modified:14 Mar 2022 13:49


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