動畫資料之製作成本甚高，因此如何有效的重複使用既有動畫資料是目前重要研究課題之一。在重複使用既有的資料之前，首要任務即是先了解其內容。此外，由不同角度觀察同一個資料往往也會得到不同的內容資訊。因此，我們提出萃取簡要且具代表性的動畫資訊來協助使用者能快速對繁瑣的動畫資料有更明確清楚地瞭解。在本論文中，我們首先將各種動畫特徵歸納為兩類：代表屬性 (representative attributes) 以及隱性知識 (implicit knowledge)。代表屬性為動畫的視覺特徵而隱性知識則是潛在的關係。

在本篇論文的第一部份，我們針對代表屬性中的關鍵畫格 (keyframe) 提出兩種萃取方法，分別為Key Probe和FrameRank。第一種方法我們將萃取關鍵畫格視為一個矩陣分解的問題 (matrix factorization problem) 並採用最小平方法(least-squares optimization) 來求得結果。此外，本論文基於動畫畫格的屬性提出第二個稱之為FrameRank的方法。此方法利用畫格屬性來評量畫格重要性，並且依據得到的畫格重要性來萃取關鍵畫格。上述兩種方法可適用於各種動畫資料，包含了多物件動畫 (multiple objects)、剛性 (rigid-body) 和軟性 (soft-body) 的動畫物件以及會隨著時間改變結構 (time-varying structure) 的動畫物件 (例如煙火)。

本論文第二部份則是提出發掘人體動作隱性知識的方法。此方法的主要目的為展示不同動作中人體各部位的關係。其關鍵點在於針對人體動作提出一個符號表示法 (symbolic encoding scheme)。符號表示法可以有效的將人體動作表示為動作符號 (verbal symbol) 且包含動作的時間和空間上變化關係。本論文根據動作符號提出兩種視覺表示法 (visual representation) 來視覺化 (visualize) 動作資料。第一種表示法稱之為動作植物 (motion plant)。可以用來呈現動作中不同身體部位的動作先後關係。第二種表示法稱之為動作圖像 (motion icon)。此方式採用統計的觀點來展示動作中隱含的資訊。根據實驗顯示，本論文所提出之兩種視覺化方法不僅可以發掘和呈現動作中隱性的資訊，還可以用來了解人體動作裡各個身體部位的動作協調程度 (motion synergy)。

Prior to reusing existing data, the most fundamental task is to understand the essence of the data. Furthermore, observing given data from distinct perspectives will provide different insights. Therefore, we seek to extract informative knowledge of animations for obtaining better clarity and usability. In this dissertation, we classify various animation features into two kinds of characteristics, representative attributes and implicit knowledge. Representative attributes denote the elements that can convey visual characteristics of animations, and implicit knowledge refers to those correlations and rules which potentially exist but are not presently evident.

To obtain representative attributes, we present two animation summarization methods for extracting the keyframes of animations. The first approach is a constraint-based technique, called Key Probe, which casts the keyframe extraction problem as a constrained matrix factorization problem and solves the problem based on the least-squares optimization technique. The second method, called FrameRank, considers the frame dependent properties to evaluate frame importance and extract salient frames. Experiments with various types of animation examples show that the proposed method produces satisfying results. This method improves upon previous work in that it can deal with multiple objects, handle both rigid-body and soft-body animations, and cope with more complex animations that have a time-varying structure (e.g., fractures and fireworks).

To discover the implicit knowledge of human motions, we present a novel visual analysis approach for revealing the relation between individual motions of different body portions. The kernel of our analysis method is a symbolic encoding scheme that represents the motion patterns with verbal symbols, effectively characterizing the spatio-temporal variation of the given motion. Two new visual representations are built on the basis of the verbal symbols: a motion plant that sequentially provides a detailed characterization of the motion, and a motion icon that statistically summarizes the comparisons between verbal streams of body portions. Experimental results show that our approach not only reveals the implicit relationships of human motions, but also favors comparative visualization of human motions in the context of the motion synergy study.

Alexa, M. & Muller, W. (2000). Representing animations by principal components. Computer Graphics Forum, 19, 411-418.

Arikan, O. & Forsyth, D.A. (2002). Interactive motion generation from examples. ACM Transactions on Graphics, 21, 483-490.

Arikan, O., Forsyth, D.A. & O'Brien, J.F. (2003). Motion synthesis from annotations. ACM Transactions on Graphics, 22, 402-408.

Assa, J., Caspi, Y. & Cohen-Or, D. (2005). Action synopsis: pose selection and illustration. ACM Trans. Graph., 24, 667-676.

Aubry, M., Julliard, F. & Gibet, S. (2009). Modeling joint synergies to synthesize realistic movements. In Proceedings of The 8th International Gesture Workshop.

Baak, A., Mueller, M. & Seidel, H.P. (2008). An effcient algorithm for keyframe-based motion retrieval in the presence of temporal deformations. In MIR '08: Proceeding of the 1st ACM international conference on Multimedia information retrieval , 451-458.

Beaudoin, P., Coros, S., van de Panne, M. & Poulin, P. (2008). Motion-Motif Graphs. In Eurographics/SIGGRAPH Symposium on Computer Animation, 117-126.

Biological Modeling and Visualization group (2010). The virtual laboratory:L-studio.

Blanz, V. & Vetter, T. (1999). A morphable model for the synthesis of 3d faces. In Proceedings of the 26th annual conference on Computer graphics and interactive techniques, 187-194.

Bouvier-Zappa, S., Ostromoukhov, V. & Poulin, P. (2007). Motion cues for illustration of skeletal motion capture data. In Proceedings of the 5th international symposium on Non-photorealistic animation and rendering, 133-140.

Brand, M. & Hertzmann, A. (2000). Style machines. In SIGGRAPH '00: Proceedings of the 27th annual conference on Computer graphics and interactive techniques, 183-192.

Briceno, H.M., Sander, P.V., McMillan, L., Gortler, S. & Hoppe, H. (2003). Geometry videos: a new representation for 3d animations. In Proceedings of the 2003 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 136-146.

Brown, W.M., Cronk, L., Grochow, K., Jacobson, A., Liu, C.K., Popovi_c, Z. & Trivers, R. (2005). Dance reveals symmetry especially in young men. Nature, 438, 1148-1150.

Bulut, E. & Capin, T. (2007). Key frame extraction from motion capture data by curve saliency. In International Conference on Computer Animation
and Social Agents.

Carnegie Mellon University (2010). Motion capture database.

Chao, S.P., Chiu, C.Y., Yang, S.N. & Lin, T.G. (2004). Tai chi synthesizer: A motion synthesis framework based on key-postures and motion instructions. Computer Animation and Virtual Worlds, 15, 259-268.

Charbonnier, C., Assassi, L., Volino, P. & Magnenat-Thalmann, N. (2009). Motion study of the hip joint in extreme postures. The Visual Computer, 25, 873-882.

d'Avella, A., Saltiel, P. & Bizzi, E. (2003). Combinations of muscle synergies in the construction of a natural motor behavior. Nature Neuroscience, 6, 300-308.

DeCarlo, D., Finkelstein, A., Rusinkiewicz, S. & Santella, A. (2003). Suggestive contours for conveying shape. ACM Trans. Graph., 22, 848-855.

Dipietro, L., Krebs, H.I., Fasoli, S.E., Volpe1, B.T., Stein, J., Bever, C. & Hogan, N. (2007). Changing motor synergies in chronic stroke. Journal of Neurophysiology, 98, 757-768.

Fellbaum, C., ed. (1998). WordNet: An Electronic Lexical Database. The MIT Press.

Geng, L. & Hamilton, H.J. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38, 9.

Giese, M.A., Mukovskiy, A., Park, A.N., Omlor, L. & Slotine, J.J.E. (2009). Real-time synthesis of body movements based on learned primitives. In Statistical and Geometrical Approaches to Visual Motion Analysis, Lecture Notes in Computer Science, 5604, 107-127.

Girgensohn, A. & Boreczky, J. (2000). Time-constrained keyframe selection technique. Multimedia Tools and Applications, 11, 347-358.

Golub, G.H. & Loan, C.F.V. (1996). Matrix Computations. Johns Hopkins University Press.

Gonzalez, R.C. & Woods, R.E. (2002). Digital Image Processing. Prentice Hall, 2nd edn.

Guskov, I. & Khodakovsky, A. (2004). Wavelet compression of parametrically coherent mesh sequences. In Proceedings of the 2004 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 183-192.

Han, J. & Kamber, M. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann.

Holton, M. (1994). Strands, gravity and botanical tree imagery. Computer Graphics Forum, 13, 57-67.

Hori, C. & Furui, S. (2003). A new approach to automatic speech summarization. IEEE Transactions on Multimedia, 5, 368-378.

Horton, W.K. (1994). The ICON Book: Visual Symbols for Computer Systems and Documentation. John Wiley & Sons, Inc.

Hu, Y., Wu, S., Xia, S., Fu, J. & Chen, W. (2010). Motion track: Visualizaing motion variation of human motion data. In Proceedings of IEEE Pacific Visualization Symposium, 153-160.

Huang, K.S., Chang, C.F., Hsu, Y.Y. & Yang, S.N. (2005). Key probe: A technique for animation keyframe extraction. The Visual Computer, 21, 532-541.

Hurri, J., Gavert, H., Sarela, J. & Hyvarinen, A. (2004). FastICA. http://www.cis.hut._/projects/ica/fastica/.

Hyvarinen, A., Karhunen, J. & Oja, E. (2001). Independent Component Analysis. John Wiley & Sons.

Jolliffe, I.T. (2002). Principal Component Analysis. Springer-Verlag.

Joshi, A. & Rheingans, P. (2005). Illustration-inspired techniques for visualizing time-varying data. In Proceedings of the IEEE Visualization 2005 , 679-686.

Kleiberg, E., van de Wetering, H. & Van Wijk, J.J. (2001). Botanical visualization of huge hierarchies. In Proceedings of the IEEE Symposium on Information Visualization 2001 , 87-94.

Kolhoff, P., Preu_, J. & Loviscach, J. (2008). Content-based icons for music files. Computers & Graphics, 32, 550-560.

Kovar, L. & Gleicher, M. (2004). Automated extraction and parameterization of motions in large data sets. ACM Transactions on Graphics, 23, 559-568.

Kovar, L., Gleicher, M. & Pighin, F. (2002). Motion graphs. ACM Transactions on Graphics, 21, 473-482.

Lee, J., Chai, J., Reitsma, P.S.A., Hodgins, J.K. & Pollard, N.S. (2002). Interactive control of avatars animated with human motion data. ACM Transactions on Graphics, 21, 491-500.

Lee, K.H., Choi, M.G. & Lee, J. (2006). Motion patches: building blocks for virtual environments annotated with motion data. ACM Transactions on Graphics, 25, 898-906.

Lee, T.Y., Lin, C.H., Wang, Y.S. & Chen, T.G. (2008). Animation keyframe extraction and simpli_cation using deformation analysis. IEEE Transactions on Circuits and Systems for Video Technology, 18, 478-486.

Lim, I.S. & Thalmann, D. (2001a). Key-posture extraction out of human motion data by curve simpli_cation. In Proceedings of 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2001), 1167-1169.

Lim, I.S. & Thalmann, D. (2001b). A vector-space representation of motion data for example-based motion synthesis. In Proceedings of the IFIP TC5/WG5.10 DEFORM'2000 Workshop and AVATARS'2000 Workshop on Deformable Avatars, 169-179.

Liu, C.K., Hertzmann, A. & Popovi_c, Z. (2005). Learning physics-based motion style with nonlinear inverse optimization. ACM Transactions on Graphics, 24, 1071-1081.

Liu, F., Zhuang, Y., Wu, F. & Pan, Y. (2003). 3D motion retrieval with motion index tree. Computer Vision and Image Understanding, 92, 265-284.

Matsuda, K. & Kondo, K. (2004). Keyframes extraction method for motion capture data. Journal for Geometry and Graphics, 8, 81-90.

McCloud, S. (1994). Understanding Comics: The Invisible Art . Harper Paperbacks.

Meng, J., Yuan, J., Hans, M. & Wu, Y. (2008). Mining motifs from human motion. In Eurographics 2008 - Short Papers, 71-74.

Muller, M., Baak, A. & Seidel, H.P. (2009). E_cient and robust annotation of motion capture data. In SCA '09: Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation, 17-26.

Muller, W. & Schumann, H. (2003). Visualization methods for timedependent data - an overview. In Proceedings of the 35th conference on Winter simulation, 737-745.

Nienhaus, M. & Dollner, J. (2005). Depicting dynamics using principles of visual art and narrations. IEEE Computer Graphics and Applications, 25, 40-51.

Onder, O., Gudukbay, U., Ozguc, B., Erdem, T., Erdem, C. & Ozkan, M. (2008). Keyframe reduction techniques for motion capture data. In 3DTV Conference: The True Vision - Capture, Transmission and Display of 3D Video, 293-296.

Parent, R. (2001). Computer Animation: Algorithms and Techniques. Morgan Kaufmann.

Park, M.J. & Shin, S.Y. (2004). Example-based motion cloning. Computer Animation and Virtual Worlds, 15, 245-257.

Ren, L., Patrick, A., Efros, A.A., Hodgins, J.K. & Rehg, J.M. (2005). A data-driven approach to quantifying natural human motion. ACM Transactions on Graphics, 24, 1090-1097.

Rother, C., Bordeaux, L., Hamadi, Y. & Blake, A. (2006). Autocollage. ACM Trans. Graph., 25, 847-852.

Setlur, V., Albrecht-Buehler, C., Gooch, A., Rossoff, S. & Gooch, B. (2005). Semanticons: Visual metaphors for _les. Computer Graphics Forum, 24, 647-656.

Shamir, A., Rubinstein, M. & Levinboim, T. (2006). Generating comics from 3d interactive computer graphics. IEEE Computer Graphics and Applications, 26, 53-61.

Shelton, C.R. (2000). Morphable surface models. International Journal of Computer Vision, 38, 75-91.

Shilane, P. & Funkhouser, T. (2007). Distinctive regions of 3D surfaces. ACM Trans. Graph., 26, 7:1-7:15.

Simon, I., Snavely, N. & Seitz, S.M. (2007). Scene summarization for online image collections. In IEEE 11th International Conference on Computer Vision.

Torres-Oviedo, G. & Ting, L.H. (2007). Muscle synergies characterizing human postural responses. Journal of Neurophysiology, 98, 2144-2156.

Truong, B.T. & Venkatesh, S. (2007). Video abstraction: A systematic review and classi_cation. ACM Trans. Multimedia Comput. Commun. Appl., 3, 3.

van Wijk, J.J., van Ham, F. & van de Wetering, H. (2003). Rendering hierarchical data. Communications of the ACM, 46, 257-263.

Whitaker, H. & Halas, J. (1981). Timing for Animation. Focal Press.

Winkler, T., Drieseberg, J., Hasenfu_, A., Hammer, B. & Hormann, K. (2008). Thinning mesh animations. In Proceedings of Vision, Modeling, and Visualization 2008 , 149-158.

Xiao, J., Zhuang, Y., Yang, T. & Wu, F. (2006). An e_cient keyframe extraction from motion capture data. In Computer Graphics International 2006 , 494-501.

Xiao, J., Zhuang, Y., Wu, F., Guo, T. & Liang, Z. (2010). A group of novel approaches and a toolkit for motion capture data reusing. Multimedia Tools and Applications, 47, 379-408.

Xu, C., Maddage, N.C. & Shao, X. (2005). Automatic music classi_cation and summarization. IEEE Transactions on Speech and Audio Processing, 13, 441-450.