A semantic feature for human motion retrieval

This source preferred by Jian Jun Zhang and Xiaosong Yang

This data was imported from Scopus:

Authors: Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X. and Zhang, J.

http://eprints.bournemouth.ac.uk/21412/

Journal: Computer Animation and Virtual Worlds

Volume: 24

Issue: 3-4

Pages: 399-407

eISSN: 1546-427X

ISSN: 1546-4261

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.

This data was imported from Web of Science (Lite):

Authors: Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X. and Zhang, J.

http://eprints.bournemouth.ac.uk/21412/

Journal: COMPUTER ANIMATION AND VIRTUAL WORLDS

Volume: 24

Issue: 3-4

Pages: 399-407

eISSN: 1546-427X

ISSN: 1546-4261

DOI: 10.1002/cav.1505

The data on this page was last updated at 05:24 on October 24, 2020.