A semantic feature for human motion retrieval
Authors: Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X. and Zhang, J.
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
Abstract: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.
https://eprints.bournemouth.ac.uk/21412/
Source: Scopus
Preferred by: Jian Jun Zhang and Xiaosong Yang
A semantic feature for human motion retrieval
Authors: Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X. and Zhang, J.
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
https://eprints.bournemouth.ac.uk/21412/
Source: Web of Science (Lite)
A semantic feature for human motion retrieval
Authors: Qi, T., Feng, Y., Xiao, J., Zhuang, Y., Yang, X. and Zhang, J.J.
Journal: Computer Animation and Virtual Worlds
Volume: 24
Issue: 3-4
Pages: 399-407
ISSN: 1546-4261
Abstract: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.
https://eprints.bournemouth.ac.uk/21412/
Source: BURO EPrints