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研究生:周芸鋒
研究生(外文):Chou, Yun-Feng
論文名稱:以統計方法為基礎之二維角色動畫合成
論文名稱(外文):Statistical Approaches for 2D Character Animation
指導教授:施仁忠施仁忠引用關係
指導教授(外文):Shih, Zen-Chung
學位類別:博士
校院名稱:國立交通大學
系所名稱:資訊科學與工程研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2010
畢業學年度:98
語文別:英文
論文頁數:111
中文關鍵詞:影像形變無母數迴歸橢圓徑向基底函數函數近似貝氏推論時間序列
外文關鍵詞:Image deformationNonparametric regressionElliptic radial basis functionsFunctional approximationBayesian inferenceTime series
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  • 被引用被引用:0
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傳統二維動畫製作是屬於一個勞動密集型態的製作過程,也就是以人工手繪的方式逐格繪製該動畫中每一格人物角色的姿勢,且以固定的畫面更新率,產生該人物角色的動作或行為,而製作過程中,耗費大量的人力與物力在繪製每格人物角色的姿勢,與為其所繪製之姿勢進行上色工作。為了節省上述傳統二維動畫製作所耗費的人力與成本,本論文提出一個新的動畫合成方法,取代傳統人工手繪的方式,我們的方法以統計分析與推論為基礎,以較為有限的人工介入,來合成逼真的二維角色動畫。我們透過統計學中的無母數迴歸分析,有效率地描述靜態影像中,預先採樣的角色位移資訊,藉此合成該靜態影像中角色的二維動畫。此外,二維角色動畫可以被視為一個三維的空間與時間轉換問題,我們根據數張連續的靜態影像中的同一人物,研究其在不同時間點個別姿勢之相對關係,我們採用時間序列的概念,來分析與預測該角色一連串適宜的連續動作。
在本論文中,我們把二維角色動畫製作分成不同的多媒體應用,包括新視角的合成、臉部表情與說話嘴形的模擬、肢體動作合成。如上述所示,我們透過無母數迴歸,產生出由另一個視點觀看影像中人物角色的效果,且進一步模擬該角色與輸入語音同步的說話嘴形和臉部表情。針對影像中該角色的輪廓資訊,本論文將介紹一種特殊的資料參數表示式:橢圓徑向基底函數,主要用於描述於橢圓表面採樣之資訊。我們利用無母數迴歸當中的橢圓徑向基底函數核迴歸去描述並預測該人物角色形狀的改變,藉此產生角色動畫,而且,為了在角色變形之後,仍維持原有的角色細節或特徵,無母數迴歸中的局部加權迴歸則被用來加強區域細節的控制,藉此保有該角色的原有特徵。此外,我們進行時間序列分析,從數張連續影像中,針對影像中同一人物角色在不同時間點的姿勢,來分析該角色的肢體移動軌跡,我們提出一個無母數貝氏方法來估計代表該移動軌跡的時間序列,並依照所估計的時間序列,模擬該角色的行為或動作。本論文最終將更深入探討如何透過所提之統計方法來合成被動元件的動畫,也就是合成由自然界外力所造成的被動元件移動,如合成出因風吹拂,造成樹木搖曳與水起漣漪的效果。
本論文提出一個從靜態影像中,有效率地合成出二維角色動畫的方法。實驗成果充分驗證本論文所提方法之可行性與可塑性,不但能夠有效模擬出逼真的角色動作,所估計的移動軌跡能因應所提供角色不同時間點的姿勢而變化,產生出的動畫亦減少不自然的扭曲現象,另一方面,本論文所提方法特別適合於智能化的多媒體應用,可用於如虛擬人物的合成,我們也相信此方法能加速整個動畫製作的過程。
Traditionally, the production of 2D animation is a labor-intensive artisan process of building up sequences of drawn images by hand which, when shown one after the other one at a fixed rate, resemble a movement. Most work and hence time is spent on drawing, inking, and coloring the individual animated characters for each of the frames. Instead of the traditional animation generated by hand, we introduce a novel method by enhancing still pictures and making characters move in convincing ways. The proposed method is based on the statistical analysis and inference, while minimizing users’ intervention. We adopt nonparametric regression to efficiently analyze the displacements of the pre-sampled data from characters in still pictures and use it to generate 2D character animation directly. Furthermore, 2D character animation is regarded as 3D transformation problem, which consists of a 2D spatial displacement and a 1D shift in time. Hence, we focus on the temporal relationship of different poses of the same character in these still pictures. Time series is applied to analyze the character’s movement and forecast a sequence of the suitable limbs movement of the character.
In this dissertation, 2D character animation involves novel view generation, expressive talking face simulation, and limbs movement synthesis. Considering characters in still pictures, we focus on nonparametric regression to generate a novel view and an expressive facial animation synchronized with the input speech of a character. Kernel regression with elliptic radial basis functions (ERBFs) is proposed to describe and deform the shape of the character in image space. Note that the novel parametric representation, ERBFs, can be applied to represent the observations of the shape on the unit ellipse. For preserving patterns within the deformed shape, locally weighted regression (LOESS) is applied to fit the details with local control. Furthermore, time series is used to analyze the limb movement of a character and represent the motion trajectory. Note that a character’s motion could be described by a series of non-continuous poses of a character from a sequence of contiguous frames. According to these poses, we investigate a nonparametric Bayesian approach to construct the time series model representing the character’s motion trajectory. Then we can synthesize a sequence of the motion by using the motion trajectory. Last but not the least, we also investigate how to adopt the proposed statistical approaches mentioned above to animate passive elements. The movements of passive elements involving natural movements that respond to natural forces in some fashion like trees swaying and water rippling could be synthesized. Given a picture of a tree, we make it sway. Given a picture of a pond, we make it ripple.
The solutions are developed to animate photographs or paintings effectively. Experimental results show that our method effectively simulates plausible movements for 2D character animation. They also show that the estimated motion trajectory best matches the given still frames. In comparison to previous approaches, our proposed method synthesizes smooth animations, while minimizing unnatural distortion and having the advantages of being more controllable. Moreover, the proposed method is especially suitable for intelligent multimedia applications in virtual human generation. We believe that the provided solutions are easy to use, and empower a much quicker animation production.
摘 要 i
ABSTRACT iii
Acknowledgements v
Contents vi
List of Tables viii
List of Figures ix
List of Symbols xii
Chapter 1 Introduction 1
1.1 Overview of Traditional 2D Animation Production 1
1.2 Motivation 4
1.3 Methodology 4
1.4 Primary Contributions 9
1.5 Auxiliary Multimedia Application 10
1.6 Dissertation Organization 11
Chapter 2 Literature Review 12
2.1 Image Morphing 12
2.2 Shape Deformation 14
2.3 Image Interpolation 15
2.4 View Interpolation 15
2.5 Expression and Viseme Synthesis 16
2.6 Motion Capture 16
2.7 Time Series 17
Chapter 3 Statistical Approaches 19
3.1 Kernel Regression with Elliptic Radial Basis Functions 19
3.2 Locally Weighted Regression 23
3.3 Reversible Jump Markov Chain Monte Carlo (RJMCMC) Sampler 26
3.4 Time Series Analysis 28
Chapter 4 Two-scale Image Abstraction 29
4.1 Color Space Transformation 29
4.2 Bilateral Filter 31
4.3 Image Abstraction 32
4.4 Experimental Results 33
Chapter 5 Novel View Generation 36
5.1 View Interpolation 36
5.2 Algorithm Overview 38
5.3 Character Extraction 39
5.4 Shape Deformation Using Kernel Regression with ERBFs 41
5.4.1 The Determination of Initial Values 41
5.4.2 Shape Deforming 43
5.5 Detail Preservation Using LOESS 45
5.6 Experimental Results 47
Chapter 6 Expressive Face with Speech Animation 54
6.1 Character and Features Extraction 54
6.2 Speech Animation 55
6.2.1 Algorithm Overview 56
6.2.2 Viseme Synthesis 56
6.3 Viseme Synthesis with Expressive Face 58
6.4 Experimental Results 61
Chapter 7 Limbs Movement Synthesis 68
7.1 Statistic-based Movement Synthesis 68
7.2 Algorithm Overview 70
7.3 Bayesian-based Limbs Movement Synthesis 72
7.3.1 Shape Structure 72
7.3.2 Point-to-point Correspondences 73
7.3.3 Bayesian Inference 74
7.3.4 The Time Series Model 76
7.3.5 Detail Preservation 77
7.4 Summary 78
7.5 Experimental Results 79
Chapter 8 Animating Passive Elements 85
8.1 Simple Harmonic Motion 85
8.2 Algorithm Overview 87
8.3 Passive Element Animation 89
8.3.1 Extraction and Specification 89
8.3.2 Water Waves 90
8.3.3 Trees 94
8.4 Experimental Results 95
Chapter 9 Conclusion and Future Work 100
9.1 Conclusion 100
9.2 Future Work 102
Bibliography 105
Vita 111
[1] M. Alexa, D. Cohen-Or, and D. Levin, “As-rigid-as-possible shape interpolation,” In Proceeding of ACM SIGGRAPH 2000, pp. 157-164, 2000.
[2] N. Arad, N. Dyn, D. Reisfeld, and Y. Yeshurun, “Image warping by radial basis functions: applications to facial expressions,” CVGIP Graph Models Image Processing, vol. 56, no. 2, pp.161-172, 1994.
[3] S. Baker, D. Scharstein, J. P. Lewis, S. Roth, M. J. Black, and R. Szeliski, “A database and evaluation methodology for optical flow,” in Proceedings of IEEE International Conference on Computer Vision, pp. 1-8, 2007.
[4] W. Baxter and K.-I. Anjy, “Latent doodle space,” Computer Graphics Forum, vol.25, no. 3, pp. 477-485, 2006.
[5] C. M. Bishop, Neural networks for pattern recognition. MIT Press, 1995.
[6] V. Blanz, C. Basso, T. Poggio, and T. Vetter, “Reanimating faces in images and video,” Computer Graphics Forum, vol. 22, no. 3, pp. 641-650, 2003.
[7] M. Botsch and O. Sorkine, “On linear variational surface deformation methods,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 1, pp. 213-230, 2008.
[8] M. Brand, “Voice puppetry,” in Proceedings of ACM SIGGRAPH 1999, pp. 21-28, 1999.
[9] M. Brand and A. Hertzmann, “Style machines,” In Proceeding of ACM SIGGRAPH 2000, pp. 183-192, 2000.
[10] C. Busso, Z. Deng, M. Grimm, U. Neumann, and S. S. Narayanan, “Rigid head motion in expressive speech animation: analysis and synthesis,” IEEE Transactions on Audio, Speech and Language Processing, vol. 15, no. 8, pp. 1075-1086, 2007.
[11] C. Busso and S. S. Narayanan, “Interrelation between speech and facial gestures in emotional utterances: a single subject study,” IEEE Transactions on Audio, Speech and Language Processing, vol. 15, no. 8, pp. 2331-2347, 2007.
[12] J. Chai and J. K. Hodgins, “Constraint-based motion optimization using a statistical dynamic model,” ACM Transactions on Graphics, vol. 26, no. 3, article 8, 2007.
[13] C. W. S. Chen, R. E. Mcculloch, and R. S. Tsay, “A unified approach to estimating and modeling linear and nonlinear time series,” Technical Report. Gradute School of Business, University of Chicago, 1996.
[14] M.-H. Chen, Q.-M. Shao, and G. Ibrahimj, Monte Carlo methods in Bayesian computation. Springer, 2000.
[15] S. E. Chen and L. William, “View interpolation for image synthesis,” in Proceeding of ACM SIGGRAPH 1993, pp. 279-288, 1993.
[16] Y.-Y. Chuang, D. B. Goldman, K. C. Zheng, B. Curless, D. Salesin, and R. Szeliski, “Animating pictures with stochastic motion textures,” ACM Transactions on Graphics, vol. 24, no. 3, pp. 853-860, 2005.
[17] E. Chuang and C. Bregler, “Mood swings: expressive speech animation,” ACM Transactions on Graphics, vol. 24, no. 2, pp. 331-347, 2005.
[18] C.-H. Chuang, S.-F. Tsai, and C.-J. Kuo, “Cartoon animation and morphing by using the wavelet curve descriptor,” in Proceedings of 1994 IEEE International Conference on Image Processing, pp. 666-670, 1994.
[19] C. N. DeJuan and B. Bodenheimer, “Re-using traditional animation: methods for semi-automatic segmentation and inbetweening,” in Proceedings of SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 223-232, 2006.
[20] Z. Deng and U. Neumann, “Efase: expressive facial animation synthesis and editing with phoneme-isomap controls,” in Proceedings of SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 251-260, 2006.
[21] T. F. Ezzat, G. Geiger, and T. Poggio, “Trainable video realistic speech animation,” ACM Transactions on Graphics, vol. 21, no. 3, pp. 388-398, 2002.
[22] J. Fan and Q. Yao, Nonlinear time series: nonparametric and parametric methods. Springer, 2005.
[23] Z. Farbman, R. Fattal, D. Lischinski, and R. Szeliski, “Edge-preserving decompositions for multi-scale tone and detail manipulation,” ACM Transactions on Graphics, vol. 27, no. 3, article 67, 2008.
[24] S. Forstmann, J. Ohya, A. Krohn-Grimberghe, and R. McDougall, “Deformation styles for spline-based skeletal animation,” in Proceeding of SIGGRAPH/Eurographics Symposium on Computer Animation, pp. 141-150, 2007.
[25] T. Fu and H. Foroosh, “Expression morphing from distant viewpoints,” in Proceeding of the International Conference on Image Processing, pp. 3519-3522, 2004.
[26] A. Galata, N. Johnson, and D. Hogg, “Learning variable length Markov models of behavior,” Computer Vision and Image Understanding, vol. 81, no. 3, pp. 398-413, 2001.
[27] B. Glocker, N. Paragios, K. Komodakis, G. Tziritas, and N. Navab, “Optical flow estimation with uncertainties through dynamic MRFs,” in IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[28] E. Goldstein and C. Gotsman, “Polygon morphing using a multiresolution representation,” in Proceedings of Graphics Interface 1995, pp.247-254, 1995.
[29] R. Herbrich, Learning kernel classifiers theory and algorithms. The MIT Press, 2002.
[30] A. Hornung, E. Dekkers, and L. Kobbelt, “Character animation from 2D pictures and 3D motion data,” ACM Transactions on Graphics, vol. 26, no. 1, article 1, 2007.
[31] T. Igarashi, T. Moscovich, J. F. Hughes, “As-rigid-as-possible shape manipulation,” ACM Transactions on Graphics, vol. 24, no. 3, pp. 1134-1141, 2005.
[32] A. K. Jain, Fundamentals of digital image processing. Prentice-Hall, 1989.
[33] Y. Jang, R. P. Botchen, A. Lauser, D. S. Ebert, K. P. Gaither, and T. Ertl, “Enhancing the interactive visualization of procedurally encoded multifield data with ellipsoidal basis functions,” Computer Graphics Forum, vol. 25, no. 3, pp. 587-596, 2006.
[34] L. Lempitsky, S. Roth, C. Rother, “FusionFlow: discrete-continuous optimization for optical flow estimation,” in IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[35] Y. Li and D. Huttenlocher, “Learning for optical flow using stochastic optimization,” in Proceedings of the 10th European Conference on Computer Vision, no. 2, pp. 379-391, 2008.
[36] P. Litwinowicz and L. Willams, “Animating images with drawings,” in Proceeding of ACM SIGGRAPH 1994, pp. 409-412, 1994.
[37] Z. Liu, Y. Shan, and Z. Zhang, “Expressive expression mapping with ratio images,” in Proceedings of ACM SIGGRAPH 2001, pp. 271-276, 2001.
[38] S. Osher, J. A. Sethian, “Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations,” Journal of Computational Physics, vol. 79, no. 1, pp. 12-49, 1988.
[39] D. Mahajan, F.-C. Huang, W. Matusik, R. Ramamoorthi, and P. Belhumeur, “Moving gradients: a path-based method for plausible image interpolation,” ACM Transactions on Graphics, vol. 28, no. 3, article 42, 2009.
[40] H. McGurk and J. MacDonald, “Hearing lips and seeing voices,” Nature, vol. 264, no. 5588, pp. 746-748, 1976.
[41] A. Menache, Understanding motion capture for computer animation and video games. Morgan Kaufmann Publishers Inc., 1999.
[42] D. C. Montgomery, E. A. Peck, and G. G. Vining, Introduction to linear regression analysis. Wiley, 2006.
[43] R. Mukundan, S. H. Ong, and P. A. Lee, “Image analysis by tchebichef moments,” IEEE Transactions on Image Processing, vol. 10, no. 9, pp. 1357-1364, 2001.
[44] T. Ngo, D. Cutrell, J. Dan, B. Donald, L. Loeb, and S. Zhu, “Accessible animation and customizable graphics via simplicial configuration modeling,” in Proceedings of ACM SIGGRAPH 2000, pp. 403-410, 2000.
[45] L. R. Rabiner, “A tutorial on hidden markov models and selected applications in speech recognition,” Readings in speech recognition, pp. 267-296, 1990.
[46] W. J. Palm, Modeling, analysis, and control of dynamic systems. Wiley, 1998.
[47] J. Park and I. W. Sandberg, “Nonlinear approximations using elliptic basis function networks,” in Proceedings of the 32nd Conference on Decision and Control, pp. 3700-3705, 1993.
[48] V. Ranjan and A. Fournier, “Matching and interpolation of shapes using unions of circles,” Computer Graphics Forum, vol. 15, no. 3, pp.129-142, 1996.
[49] X. Ren, “Local grouping for optical flow,” in IEEE Conference on Computer Vision and Pattern Recognition, 2008.
[50] C. Rose, M. F. Cohen, and B. Bodenheimer, “Verbs and adverbs: multidimensional motion interpolation,” IEEE Computer Graphics and Applications, vol. 18, no. 5, pp. 32-40, 1998.
[51] C. Rother, V. Kolmogorov, and A. Blake, “GrabCut: interactive foreground extraction using iterated graph cuts,” ACM Transactions on Graphics, vol. 23, no. 3, pp. 309-314, 2004.
[52] D. Ruprecht and H. M?刜ler, “Image warping with scattered data interpolation,” IEEE Computer Graphics and Applications, vol. 15, no. 2, pp. 37-43, 1995.
[53] S. Schaefer, T. Mcphail, and J. Warren, “Image deformation using moving least squares,” ACM Transactions on Graphics, vol. 25, no. 3, pp. 533-540, 2006.
[54] K. Scherbaum, M. Sunkel, H.-P. Seidel, and V. Blanz, “Prediction of individual non-linear aging trajectories of faces,” Computer Graphics Forum, vol. 26, no. 3, pp. 285-294, 2007.
[55] T. Sederberg and E. Greenwood, “A physically based approach to 2D shape blending,” in Proceedings of ACM SIGGRAPH 1992, pp. 25-34, 1992.
[56] S. M. Seitz and C. R. Dyer, “View morphing,” in Proceeding of ACM SIGGRAPH 1996, pp. 21-30, 1996.
[57] J. A. Sethian, Level set methods. Cambridge University Press, 1996.
[58] J. A. Sethian, Level set methods and fast marching methods: evolving interfaces in computational geometry, fluid mechanics, computer vision, and materials science. Cambridge University Press, 1999.
[59] A. Shashua and T. Riklin-Raviv, “The quotient image: class based re-rendering and recognition with varying illuminations,” IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 129-139, 2001.
[60] R. H. Shumway and D. S. Stoffer, Time series analysis and its applications: with R examples. Springer, 2006.
[61] J. D. Shutler and M. S. Nixon, “Zernike velocity moments for sequence-based description of moving features”. Image and Vision Computing, 2006; vol. 24, no. 4, pp. 343-356, 2006.
[62] D. Sun, S. Roth, J. P. Lewis, and M. J. Black, “Learning optical flow,” in Proceedings of the 10th European Conference on Computer Vision, no. 3, pp. 83-97, 2008.
[63] B. H. Thomas and P. Calder, “Animating direct manipulation interfaces,” in Proceedings of the 8th ACM Symposium on User Interface Software and Technology, pp. 3-12, 1995.
[64] C. Tomasi and R. Manduchi, “Bilateral filtering for gray and color images,” in Proceeding of the International Conference Computer Vision 1998, pp. 839-846, 1998.
[65] W. Trobin, T. Pock, D. Cremers, H. Bischof, “Continuous energy minimization via repeated binary fusion,” in Proceedings of the 10th European Conference on Computer Vision, no. 4, pp. 677-690, 2008.
[66] S. Vedula, S. Baker, and T. Kanade, “Image-based spatio-temporal modeling and view interpolation of dynamic events,” ACM Transactions on Graphics, vol. 24, no. 2, pp. 240-261, 2005.
[67] Y. Wang, K. Xu, Y. Xiong, and Z.-Q. Cheng, “2D shape deformation based on rigid square matching,” Computer Animation and Virtual Worlds, vol. 19, no. 3-4, pp. 411-420, 2008.
[68] O. Weber, M. Ben-Chen, and C. Gotsman, “Complex barycentric coordinates with applications to planar shape deformation,” Computer Graphics Forum, vol. 28, no. 2, pp. 587-397, 2009.
[69] A. Witkin and Z. Popović, “Motion warping,” in Proceeding of ACM SIGGRAPH 1995, pages 105-108, 1995.
[70] G. Wolberg, “Image morphing: a survey,” The Visual Computer, vol. 14, no. 8-9, pp. 360-372, 1998.
[71] C. R. Wylie, Advanced engineering mathematics. McGraw-Hill, 1975.
[72] G. Wyszecki and W. S. Styles, Color science: concepts and methods, quantitative data and formulae. Wiley, 1982.
[73] L. Xu, J. Chen, and J. Jia, “Segmentation based variational model for accurate optical flow estimation,” in Proceeding of the 10th European Conference on Computer Vision, no. 1, pp. 671-684, 2008.
[74] X. Xu, L. Wan, X. Liu, T.-T. Wong, L. Wang, and C.-S. Leung, “Animating animal motion from still,” ACM Transactions on Graphics, vol. 27, no. 5, article 117, 2008.
[75] C.-R. Yan, M.-T. Chi, T.-Y. Lee, and W.-C. Lin, “Stylized rendering using samples of a painted image,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 2, pp. 468-480, 2008.
[76] H.-B. Yan, S.-M. Hu, R. R. Martin, and Y.-L. Yang, “Shape deformation using a skeleton to drive simplex transformations,” IEEE Transactions on Visualization and Computer Graphics, vol. 14, no. 3, pp. 693-706, 2008.
[77] T. Yotsukura, S. Morishima, and S. Nakamura, “Model-based talking face synthesis for anthropomorphic spoken dialog agent system,” in Proceeding of the 11th ACM International Conference on Multimedia, pp. 351-354, 2003.
[78] G. A. Young and R. L. Smith, Essentials of statistical inference. Cambridge University Press, 2005.
[79] AIC Anime International Co. Inc. http://www.aicanime.com/.
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