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Qi, T.; Feng, Yinfu; Xiao, J.; Zhuang, Yueting; Yang, Xiaosong; Zhang, Jian J. (2013)
Languages: English
Types: Article
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.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • [1] Kovar L., Gleicher M., and Pighin F. Motion graphs. ACM Transactions on Graphics (TOG), 21:473-482, 2002.
    • [2] Jain S., Ye Y., and Liu C. Optimizationbased interactive motion synthesis. ACM Transactions on Graphics (TOG), 28:1- 12, 2009.
    • [3] Heck R. and Gleicher M. Parametric motion graphs. In Proceedings of the 2007 symposium on Interactive 3D graphics and games, pages 129-136, 2007.
    • [4] Gleicher M. Motion editing with spacetime constraints. In Proceedings of the 1997 symposium on Interactive 3D graphics, pages 139-ff, 1997.
    • [5] Min J., Liu H., and Chai J. Synthesis and editing of personalized stylistic human motion. In Proceedings of the 2010 ACM SIGGRAPH symposium on Interactive 3D Graphics and Games, pages 39-46, 2010.
    • [6] Hecker C., Raabe B., Enslow R., DeWeese J., Maynard J., and Prooijen K. Real-time motion retargeting to highly varied usercreated morphologies. ACM Transactions on Graphics (TOG), 27, 2008.
    • [7] Yoshitaka A. and Ichikawa T. A survey on content-based retrieval for multimedia databases. IEEE Transactions on Knowledge and Data Engineering, 11:81- 93, 1999.
    • [8] Chao M., Lin C., Assa J., and Lee T. Human motion retrieval from handdrawn sketch. Visualization and Computer Graphics, IEEE Transactions, 18:729- 740, 2012.
    • [9] Choi M., Igarashi K., Mitani J., and Lee J. Retrieval and visualization of human motion data via stick figures. Computer Graphics Forum, 31:2057-2065, 2012.
    • [10] Lv F. and Nevatia R. Single view human action recognition using key pose matching and viterbi path searching. In Computer Vision and Pattern Recognition, pages 1-8, 2007.
    • [11] Liu F., Zhuang Y., Wu F., and Pan Y. 3d motion retrieval with motion index tree. Computer Vision and Image Understanding, 92:265-284, 2003.
    • [12] Liu G., Zhang J., Wang W., and McMillan L. A system for analyzing and indexing human-motion databases. In Proceedings of the 2005 ACM SIGMOD international conference on Management of data, pages 924-926, 2005.
    • [13] Forbes K. and Fiume E. An efficient search algorithm for motion data using weighted pca. In Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation, pages 67-76, 2005.
    • [14] Chiu C., Chao S., Wu M., Yang S., and Lin H. Content-based retrieval for human motion data. Journal of Visual Communication and Image Representation, 15:446- 466, 2004.
    • [15] Kovar L. and Gleicher M. Automated extraction and parameterization of motions in large data sets. ACM Transactions on Graphics (TOG), 23:559-568, 2004.
    • [16] Muller M., Roder T., and Clausen M. Efficient content-based retrieval of motion capture data. ACM Transactions on Graphics (TOG), 24:677-685, 2005.
    • [17] Lin Y. Efficient human motion retrieval in large databases. In Proceedings of the 4th international conference on Computer graphics and interactive techniques in Australasia and Southeast Asia, pages 31-37, 2006.
    • [18] Sun Z., Li Y., and Li Q. Partial similarity human motion retrieval based on relative geometry features. In Digital Home (ICDH), 2012 Fourth International Conference, pages 298-303, 2012.
    • [19] Yu T., Shen X., Li Q., and Geng W. Motion retrieval based on movement notation language. Computer Animation and Virtual Worlds, 16:273-282, 2005.
    • [20] Huang T., Liu H., and Ding G. Motion retrieval based on kinetic features in large motion database. In Proceedings of the 14th ACM international conference on Multimodal interaction, pages 209-216, 2012.
    • [21] Naour T., Courty N., and Gibet S. Fast motion retrieval with the distance input space. In Motion in Games Lecture Notes in Computer Science, pages 362-365, 2012.
    • [22] Muller M. and Roder T. Motion templates for automatic classification and retrieval of motion capture data. In Proceedings of the 2006 ACM SIGGRAPH/Eurographics symposium on Computer animation, pages 137-146, 2006.
    • [23] Deng Z., Gu Q., and Li Q. Perceptually consistent example-based human motion retrieval. In Proceedings of the 2009 symposium on Interactive 3D graphics and games, pages 191-198, 2009.
    • [24] Sun C., Junejo I., and Horoosh H. Motion retrieval using low-rank subspace decomposition of motion volume. Computer Graphics Forum, 30:1953-1962, 2011.
    • [25] Pradhan G. and Prabhakaran B. Indexing 3-d human motion repositories for contentbased retrieval. IEEE Transactions on Information Technology in Biomedicine, 13:802-809, 2009.
    • [26] Kruger B., Tautges J., Weber A., and Zinke A. Fast local and global similarity searches in large motion capture databases. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, pages 1-10, 2010.
    • [27] Meng J., Yuan J., Hans M., and Wu Y. Mining motifs from human motion. In InEurographics 2008 C Short Papers, pages 71-74, 2008.
    • [28] Shin H. and Igarashi T. Magic canvas: interactive design of a 3-d scene prototype from freehand sketches. In GI '07 Proceedings of Graphics Interface, pages 63- 70, 2007.
    • [29] Olsen L., Samavati F., Sousa M., and Jorge J. Sketch-based modeling: A survey. Computers & Graphics, 33:85-103, 2009.
    • [30] Gonen O. and Akleman E. Sketch based 3d modeling with curvature classification. Computers & Graphics, 36:521-525, 2012.
    • [31] Eitz M., Richter R., Boubekeur T., and Hildebrand K. Sketch-based shape retrieval. ACM Transactions on Graphics (TOG), 31, 2012.
    • [32] Shao T., Xu W., Yin K., Wang J., Zhou K., and Guo B. Sketch-based shape retrieval. Computer Graphics Forum, 30:2011-2020, 2011.
    • [33] Liu T. and Kender J. Computational approaches to temporal sampling of video sequences. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP), 3, 2007.
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  • Discovered through pilot similarity algorithms. Send us your feedback.

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