The Community for Technology Leaders
RSS Icon
Issue No.11 - November (2011 vol.17)
pp: 1676-1689
Cheng Chen , Idiap Research Institute, Martigny
Yueting Zhuang , Zhejiang University, Hangzhou
Feiping Nie , University of Texas at Arlington, Arlington
Yi Yang , ITEE, The University of Queensland, Brisbane
Fei Wu , Zhejiang University, Hangzhou
Jun Xiao , Zhejiang University, Hangzhou
Estimating 3D pose similarity is a fundamental problem on 3D motion data. Most previous work calculates L2-like distance of joint orientations or coordinates, which does not sufficiently reflect the pose similarity of human perception. In this paper, we present a new pose distance metric. First, we propose a new rich pose feature set called Geometric Pose Descriptor (GPD). GPD is more effective in encoding pose similarity by utilizing features on geometric relations among body parts, as well as temporal information such as velocities and accelerations. Based on GPD, we propose a semisupervised distance metric learning algorithm called Regularized Distance Metric Learning with Sparse Representation (RDSR), which integrates information from both unsupervised data relationship and labels. We apply the proposed pose distance metric to applications of motion transition decision and content-based pose retrieval. Quantitative evaluations demonstrate that our method achieves better results with only a small amount of human labels, showing that the proposed pose distance metric is a promising building block for various 3D-motion related applications.
Human motion, character animation, pose features, distance metric, semisupervised learning.
Cheng Chen, Yueting Zhuang, Feiping Nie, Yi Yang, Fei Wu, Jun Xiao, "Learning a 3D Human Pose Distance Metric from Geometric Pose Descriptor", IEEE Transactions on Visualization & Computer Graphics, vol.17, no. 11, pp. 1676-1689, November 2011, doi:10.1109/TVCG.2010.272
[1] J. Wang and B. Bodenheimer, “An Evaluation of a Cost Metric for Selecting Transitions between Motion Segments,” Proc. ACM SIGGRAPH/Eurographics Symp. Computer Animation, pp. 232-238, 2003.
[2] T. Harada, S. Taoka, T. Mori, and T. Sato, “Quantitative Evaluation Method for Pose and Motion Similarity Based on Human Perception,” Proc. IEEE/RAS Int'l Conf. Humanoid Robots, pp. 494-512, 2004.
[3] J. Tang, H. Leung, T. Komura, and H. Shum, “Emulating Human Perception of Motion Similarity,” Computer Animation and Virtual Worlds, vol. 19, nos. 3/4, pp. 211-221, 2008.
[4] E.S.L. Ho and T. Komura, “Indexing and Retrieving Motions of Characters in Close Contact,” IEEE Trans. Visualization and Computer Graphics, vol. 15, no. 3, pp. 481-492, May 2009.
[5] C. Chen, Y. Zhuang, J. Xiao, and Z. Liang, “Perceptual 3D Pose Distance Estimation by Boosting Relational Geometric Features,” Computer Animation and Virtual Worlds, vol. 20, nos. 2/3, pp. 267-277, 2009.
[6] F. Liu, Y. Zhuang, F. Wu, and Y. Pan, “3D Motion Retrieval with Motion Index Tree,” Computer Vision and Image Understanding, vol. 92, nos. 2/3, pp. 265-284, 2003.
[7] E. Keogh, T. Palpanas, V. Zordan, D. Gunopulos, and M. Cardle, “Indexing Large Human-Motion Databases,” Proc. Int'l Conf. Very Large Databases, pp. 780-791, 2004.
[8] E. Hsu, M. Silva, and J. Popovic, “Guided Time Warping for Motion Editing,” Proc. Eurographics/ACM SIGGRAPH Symp. Computer Animation (SCA), 2007.
[9] O. Arikan and D. Forsyth, “Motion Generation from Examples,” ACM Trans. Graphics, vol. 21, no. 3, pp. 483-490, 2002.
[10] L. Kovar, M. Gleicher, and F. Pighin, “Motion Graphs,” ACM Trans. Graphics, vol. 21, no. 3, pp. 473-482, 2002.
[11] J. Lee, J. Chai, P. Reitsma, J. Hodgins, and N. Pollard, “Interactive Control of Avatars Animated with Human Motion Data,” ACM Trans. Graphics, vol. 21, no. 3, pp. 491-500, 2002.
[12] J. Barbic, A. Safonova, J. Pan, C. Faloutsos, J.K. Hodgins, and N.S. Pollard, “Segmenting Motion Capture Data into Distinct Behaviors,” Proc. Graphics Interface, pp. 185-194, 2004.
[13] C. Lu and N.J. Ferrier, “Repetitive Motion Analysis: Segmentation and Event Classification,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 26, no. 2, pp. 258-263, Feb. 2004.
[14] O. Arikan, “Compression of Motion Capture Databases,” ACM Trans. Graphics, vol. 25, no. 3, pp. 890-897, 2006.
[15] S. Chattopadhyay, S.M. Bhandarkar, and K. Li, “Human Motion Capture Data Compression by Model-Based Indexing: A Power Aware Approach,” IEEE Trans. Visualization and Computer Graphics, vol. 13, no. 1, pp. 5-14, Jan./Feb. 2007.
[16] O. Arikan, D.A. Forsyth, and J. OBrien, “Motion Synthesis from Annotations,” ACM Trans. Graphics, vol. 33, no. 3, pp. 402-408, 2003.
[17] P.T. Chua, R. Crivella, B. Daly, H. Ning, R. Schaaf, D. Ventura, T. Camill, J. Hodgins, and R. Pausch, “Training for Physical Tasks in Virtual Environments: Tai Chi,” Proc. IEEE Virtual Reality, pp. 87-94, 2003.
[18] C. Chen, Y. Zhuang, J. Xiao, and F. Wu, “Adaptive and Compact Shape Descriptor by Progressive Feature Combination and Selection with Boosting,” Proc. IEEE Computer Soc. Conf. Computer Vision and Pattern Recognition, 2008.
[19] C.K.-F. So and G. Baciu, “Entropy-Based Motion Extraction for Motion Capture Animation: Motion Capture and Retrieval,” Computer Animation and Virtual Worlds, vol. 16, nos. 3/4, pp. 225-235, 2005.
[20] M. Muller, T. Roder, and M. Clausen, “Efficient Content-Based Retrieval of Motion Capture Data,” ACM Trans. Graphics, vol. 24, no. 3, pp. 677-685, 2005.
[21] S. Carlsson, “Combinatorial Geometry for Shape Representation and Indexing,” Proc. Object Representation in Computer Vision, pp. 53-78, 1996.
[22] J. Sullivan and S. Carlsson, “Recognizing and Tracking Human Action,” Proc. European Conf. Computer Vision, pp. 629-644, 2002.
[23] T. Mukai, K. Wakisaka, and S. Kuriyama, “Generating Concise Rules for Retrieving Human Motions from Large Data Sets,” Proc. Computer Animation and Social Agents (CASA '09), 2009.
[24] L. Kovar and M. Gleicher, “Automated Extraction and Parameterization of Motions in Large Data Sets,” ACM Trans. Graphics, vol. 23, no. 3, pp. 559-568, 2004.
[25] M. Muller and T. Roder, “Motion Templates for Automatic Classification and Retrieval of Motion Capture Data,” Proc. ACM SIGGRAPH/Eurographics Symp. Computer Animation, pp. 137-146, 2006.
[26] K. Onuma, C. Faloutsos, and J.K. Hodgins, “FMDistance: A Fast and Effective Distance Function for Motion Capture Data,” Proc. Eurographics, 2008.
[27] L. Yang and R. Jin, “Distance Metric Learning: A Comprehensive Survey,” technical report, Michigan State Univ., 2006.
[28] E. Xing, A. Ng, M. Jordan, and S. Russell, “Distance Metric Learning with Application to Clustering with Side-Information,” Proc. Advances in Neural Information Processing Systems 15, pp. 505-512, 2003.
[29] S. Xiang, “Learning a Mahalanobis Distance Metric for Data Clustering and Classification,” Pattern Recognition, vol. 41, no. 12, pp. 3600-3612, 2008.
[30] K.Q. Weinberger and L.K. Saul, “Distance Metric Learning for Large Margin Nearest Neighbor Classfication,” J. Machine Learning Research, vol. 10, pp. 207-244, 2009.
[31] M.H. Nguyen and F. Torre, “Metric Learning for Image Alignment,” Int'l J. Computer Vision, vol. 88, pp. 69-84, 2009.
[32] Y. Yang, Y. Zhuang, D. Xu, Y. Pan, D. Tao, and S.J. Maybank, “Retrieval Based Interactive Cartoon Synthesis Via Unsupervised Bi-Distance Metric Learning,” Proc. ACM Multimedia, pp. 311-320, 2009.
[33] X. Zhu, “Semi-Supervied Learning Literature Survey,” Computer Sciences technical report, Univ. of Wisconsin-Madison, 2011.
[34] D. Cai, X. He, and J. Han, “Semi-Supervised Discriminant Analysis,” Proc. IEEE Int'l Conf. Computer Vision, 2007.
[35] R. Tibshirani, “Regression Shrinkage and Selection via the LASSO,” J. Royal Statistical Soc., vol. 58, no. 1, pp. 267-288, 1996.
[36] D. Donoho, “For Most Large Underdetermined Systems of Linear Equations the Minimal L1-Norm Solution is also the Sparsest Solution,” Comm. Pure and Applied Math., vol. 59, no. 6, pp. 797-829, 2006.
[37] J. Wright, Y. Ma, J. Mairal, G. Spairo, T. Huang, and S. Yan, “Sparse Representation for Computer Vision and Pattern Recognition,” Proc. IEEE Int'l Conf. Computer Vision, 2009.
[38] J. Wright, A.Y. Yang, A. Ganesh, S.S. Sastry, and Y. Ma, “Robust Face Recognition via Sparse Representation,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 31, no. 2, pp. 210-227, Feb. 2009.
[39] F. Nie, S. Xiang, and C. Zhang, “Neighborhood MinMax Projections,” Proc. Int'l Joint Conf. Artificial Intelligence, pp. 993-998, 2007.
[40] S. Yan, D. Xu, B. Zhang, H. Zhang, Q. Yang, and S. Lin, “Graph Embedding and Extensions: A General Framework for Dimensionality Reduction,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 29, no. 1, pp. 40-51, Jan. 2009.
[41] J. Tenenbaum, V. de Silva, and J. Langford, “A Global Geometric Framework for Dimensionality Reduction,” Science, vol. 290, no. 5500, pp. 2319-2323, 2000.
[42] S. Roweis and L. Saul, “Nonlinear Dimensionality Reduction by Locally Linear Embedding,” Science, vol. 290, no. 22, pp. 2323-2326, 2000.
[43] Y. Yang, D. Xu, F. Nie, S. Yan, and Y. Zhuang, “Image Clustering Using Local Discriminant Models and Global Integration,” to be published in IEEE Trans. Image Processing.
[44] R. Duda, P. Hart, and D. Stork, Pattern Classification, secoond ed. Wiley-Interscience, 2000.
[45] H. Wang, S. Yan, D. Xu, X. Tang, and T. Huang, “Trace Ratio vs. Ratio Trace for Dimensionality Reduction,” Proc. IEEE Int'l Conf. Computer Vision, 2007.
[46] http:/, 2011.
[47] P. Viola and M. Jones, “Rapid Object Detection Using a Boosted Cascade of Simple Features,” Proc. IEEE Int'l Conf. Computer Vision, 2001.
[48] R.A. Fisher, “The Use of Multiple Measurements in Taxonomic Problems,” Annals of Eugenics, vol. 7, pp. 179-188, 1936.
17 ms
(Ver 2.0)

Marketing Automation Platform Marketing Automation Tool