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  1. NTU Theses and Dissertations Repository
  2. 電機資訊學院
  3. 資訊網路與多媒體研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53935
完整後設資料紀錄
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dc.contributor.advisor歐陽明
dc.contributor.authorI Chiangen
dc.contributor.author蔣易zh_TW
dc.date.accessioned2021-06-16T02:33:56Z-
dc.date.available2015-07-30
dc.date.copyright2015-07-30
dc.date.issued2015
dc.date.submitted2015-07-28
dc.identifier.citation[1] P. Hamalainen, S. Eriksson, E. Tanskanen, V. Kyrki, and J. Lehtinen, “Online motion synthesis using sequential Monte Carlo,” ACM Trans. Graph., vol. 33, pp. 1-12, 2014.
[2] V. B. Zordan, and J. K. Hodgins, “Motion capture-driven simulations that hit and react,” in Proceedings of the 2002 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp. 89-96, 2002.
[3] J. Lee, and K. H. Lee, “Precomputing avatar behavior from human motion data,” in Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation, pp. 79-87, 2004.
[4] R. Fattal, and D. Lischinski, “Pose controlled physically-based motion,” Computer Graphics Forum, pp. 1–11, 2006.
[5] S. Levine, J. M. Wang, A. Haraux, Z. Popović, and V. Koltun, “Continuous character control with low-dimensional embeddings,” ACM Trans. Graph., vol. 31, no. 4, pp. 1-10, 2012.
[6] S. Jain, Y. Ye, and C. K. Liu, “Optimization-based interactive motion synthesis,” ACM Trans. Graph., vol. 28, no. 1, pp. 1-12, 2009.
[7] J. Tan, Y. Gu, G. Turk, and C. K. Liu, “Articulated swimming creatures,” ACM Trans. Graph., vol. 30, no. 4, pp. 1-12, 2011.
[8] I. Mordatch, E. Todorov, Z. Popović, “Discovery of complex behaviors through contact-invariant optimization,” ACM Trans. Graph., vol. 31, no. 4, pp. 1-8, 2012.
[9] M. Al Borno, M. De Lasa, and A. Hertzmann, “Trajectory optimization for full-body movements with complex contacts,” IEEE Trans. Vis. Comput. Graph., vol. 19, no. 8, pp. 1405–1414, 2013.
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[13] H. P. H. Shum, T. Komura, and S. Yamazaki, “Simulating multiple character interactions with collaborative and adversarial goals,” IEEE Trans. Vis. Comput. Graph., vol. 18, no. 5, pp. 741–752, 2012.
[14] E. S. L. Ho, J. C. P. Chan, T. Komura, and H. Leung, “Interactive partner control in close interactions for real-time applications,” ACM Trans. Multimedia Comput. Commun. Appl., vol. 9, no. 3, pp. 1-19, 2013.
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[16] J. Hwang, I. H. Suh, and T. Kwon, “Editing and synthesizing two-character motions using a coupled inverted pendulum model,” Computer Graphics Forum, vol. 33, no. 7, pp. 21–30, 2014.
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[22] G. Guennebaud, B. Jacob, et al. Eigen v3. http://eigen.tuxfamily.org, 2010.
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[25] T.-H. Li, “Measurement and Analysis of Two Types of Chinese Martial Arts,” M.S. thesis, National Taiwan University, 2014.
[26] I. Mordatch, J. M. Wang, E. Todorov, and V. Koltun, “Animating human lower limbs using contact-invariant optimization,” ACM Trans. Graph., vol. 32, no. 6, pp. 1-8, 2013.
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[28] Y. Lee, M. S. Park, T. Kwon, and J. Lee, “Locomotion control for many-muscle humanoids,” ACM Trans. Graph., vol. 33, no. 6, pp. 1-11, 2014.
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dc.identifier.urihttp://tdr.lib.ntu.edu.tw/jspui/handle/123456789/53935-
dc.description.abstract本論文提出一基於連續蒙特卡羅法之近身搏擊動作生成,我們是使用物理模擬的方法並不依靠動態捕捉資料,人物的行為可以被分類為攻擊和防禦並且可以被寫成一個目標函數。在攻擊模式中,人物致力於打擊對手身體的重要部位,目標的身體部位是根據詠春拳法的中線理論而定義,另一方面,防禦模式的主要目的是要阻擋對手的出拳以避免造成自身重要部位的傷害。我們運用連續蒙特卡羅取樣的方法去找到最佳的控制策略,欲求解的最佳化變數是各個關節角度的運動軌跡,在我們系統中的每個樣本含有一個座標和一個目標函數值。我們方法起初會均勻地在參數空間中產生一組樣本點,接著我們進行修整只保留下一些分數較高的樣本點並由他們建立一棵k-d樹。隨後,我們使用適應性重要性抽樣從舊的樣本點中取出新的樣本點,由於每個樣本點都有一個高斯分布以之為中心,k-d樹可被視為一個高斯分布的總和,新的樣本是由隨機產生的變數根據逆高斯分布得到該座標。我們將新的樣本點代入物理引擎得到每個剛體的位置、速度和碰撞等資訊,最後依照這些物理模擬的數據,我們可以進行目標函數評估來決定新樣本點的分數,新樣本會被加入k-d樹中直到達到一定的數量後再重複上述的步驟,藉由演化後我們的結果可以呈現一些簡單的攻擊、防禦和互動等動作。zh_TW
dc.description.abstractThis thesis presents a method to synthesize close combat using sequential Monte Carlo. We perform physics-based simulation without motion capture data. The behavior of the character can be classified into attack and defense and is formulated as an objective function. In the attack mode, character aims to hit the critical body regions of the opponent. On the other hand, the main goal of defense is to block the opponent’s blow in order to prevent injuries to critical regions. These principles are designed according to fundamental theory of Chinese martial arts. We use a kd-tree sequential Monte Carlo sampler to find the optimal joint angle trajectories for each character. Each sample in our system contains a coordinate and an objective function value. For each iteration, pruning is performed to keep a few samples with the highest objective function values. Then, a kd-tree is constructed based on those remaining samples. Next, adaptive important sampling is applied to draw new samples from the old ones. Subsequently, we will feed the new generated sample into physics engine to get positions, velocities and contacts of rigid bodies. Finally, by using the information from the physics simulation, we are able to make evaluation on the objective function to determine the scores of the new samples. These samples are added to the kd-tree until a budget is reached, then we will repeat the above steps. Through the evolution, our results can show some simple attack, defense and interaction movements.en
dc.description.provenanceMade available in DSpace on 2021-06-16T02:33:56Z (GMT). No. of bitstreams: 1
ntu-104-R02944001-1.pdf: 1102679 bytes, checksum: d822e421946a16e6042e549c248f44f9 (MD5)
Previous issue date: 2015
en
dc.description.tableofcontents誌謝 i
中文摘要 ii
ABSTRACT iii
CONTENTS iv
LIST OF FIGURES vi
LIST OF TABLES viii
Chapter 1 Introduction 1
1.1 Background and Motivation 1
1.2 Proposed Solution 2
1.3 Contributions 3
1.4 Thesis Organization 3
Chapter 2 Related Work 4
2.1 Data-Driven Motion Control 4
2.2 Optimization-Based Motion Synthesis 5
2.3 Multi-Character Interactions 7
Chapter 3 Method 10
3.1 System Overview 10
3.2 Objective Function 12
3.3 Parameterization 14
3.4 Kd-tree Sequential Monte Carlo Sampler 16
Chapter 4 Experiments and Results 20
4.1 Implementation Environment 20
4.2 Results 23
4.3 Limitations 29
Chapter 5 Conclusion and Future Work 30
5.1 Conclusion 30
5.2 Future Work 30
Bibliography 31
Resume 34
dc.language.isoen
dc.subject最佳化zh_TW
dc.subject動畫zh_TW
dc.subject動作生成zh_TW
dc.subject動作規劃zh_TW
dc.subject連續蒙特卡羅法zh_TW
dc.subject粒子濾波器zh_TW
dc.subjectparticle filteren
dc.subjectoptimizationen
dc.subjectanimationen
dc.subjectmotion synthesisen
dc.subjectmotion planningen
dc.subjectsequential Monte Carloen
dc.title基於連續蒙特卡羅法之近身搏擊動作生成zh_TW
dc.titleSynthesizing Close Combat Using Sequential Monte Carloen
dc.typeThesis
dc.date.schoolyear103-2
dc.description.degree碩士
dc.contributor.oralexamcommittee梁容輝,林奕成
dc.subject.keyword動畫,動作生成,動作規劃,連續蒙特卡羅法,粒子濾波器,最佳化,zh_TW
dc.subject.keywordanimation,motion synthesis,motion planning,sequential Monte Carlo,particle filter,optimization,en
dc.relation.page34
dc.rights.note有償授權
dc.date.accepted2015-07-28
dc.contributor.author-college電機資訊學院zh_TW
dc.contributor.author-dept資訊網路與多媒體研究所zh_TW
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