Research papers (among which to choose for paper presentation) - updated for 2021/2022
Crowd simulation
Paper #1: Guy, S. J., Chhugani, J., Curtis, S., Dubey, P., Lin, M., & Manocha, D. (2010, July). Pledestrians: a least-effort approach to crowd simulation. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics symposium on computer animation (pp. 119-128). Eurographics Association.
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Paper #2: Singh, S., Kapadia, M., Reinman, G., & Faloutsos, P. (2011). Footstep navigation for dynamic crowds. Computer Animation and Virtual Worlds, 22(2‐3), 151-158.
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Paper #3: Shao, W., & Terzopoulos, D. (2005, July). Autonomous pedestrians. In Proceedings of the 2005 ACM SIGGRAPH/Eurographics symposium on Computer animation (pp. 19-28). ACM.
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Character animation
Paper #4: Victor Brian Zordan, Anna Majkowska, Bill Chiu, and Matthew Fast. 2005. Dynamic response for motion capture animation. ACM Trans. Graph. 24, 3 (July 2005), 697-701. DOI: https://doi.org/10.1145/1073204.1073249
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Paper #5: Jing Wang and Bobby Bodenheimer. 2004. Computing the duration of motion transitions: an empirical approach. In Proceedings of the 2004 ACM SIGGRAPH/Eurographics symposium on Computer animation (SCA '04). DOI: https://doi.org/10.1145/1028523.1028568
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Paper #6: Rachel Heck and Michael Gleicher. 2007. Parametric motion graphs. In Proceedings of the 2007 symposium on Interactive 3D graphics and games (I3D '07). DOI=http://dx.doi.org/10.1145/1230100.1230123
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Paper #7:Byungkuk Choi, Roger Blanco i Ribera, J. P. Lewis, Yeongho Seol, Seokpyo Hong, Haegwang Eom, Sunjin Jung, and Junyong Noh. 2016. SketchiMo: sketch-based motion editing for articulated characters. ACM Trans. Graph. 35, 4, Article 146 (July 2016), 12 pages.
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Paper #8:Eom, H., Choi, B., Cho, K., Jung, S., Hong, S. and Noh, J. (2020), Synthesizing Character Animation with Smoothly Decomposed Motion Layers. Computer Graphics Forum, 39: 595-606.
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Paper #9:Aristidou, A., Cohen-Or, D., Hodgins, J. K., Chrysanthou, Y., & Shamir, A. (2018). Deep motifs and motion signatures. ACM Transactions on Graphics (TOG), 37(6), 1-13.
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Paper #10:Daniel Holden, Taku Komura, and Jun Saito. 2017. Phase-functioned neural networks for character control. ACM Trans. Graph. 36, 4, Article 42 (July 2017), 13 pages.
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Paper #11:Richard Kulpa, Franck Multon, Bruno Arnaldi. 2005. Morphology‐independent representation of motions for interactive human‐like animation. Computer Graphics Forum 24 (3), 343-351.
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Paper #12:Wanli Ma, Shihong Xia, Jessica K. Hodgins, Xiao Yang, Chunpeng Li, and Zhaoqi Wang. 2010. Modeling style and variation in human motion. In Proceedings of the 2010 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '10). Eurographics Association, Goslar, DEU, 21–30.
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Paper #13:Jianyuan Min and Jinxiang Chai. 2012. Motion graphs++: a compact generative model for semantic motion analysis and synthesis. ACM Trans. Graph. 31, 6, Article 153 (November 2012), 12 pages.
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Paper #14:Hubert P. H. Shum, Taku Komura, Masashi Shiraishi, and Shuntaro Yamazaki. 2008. Interaction patches for multi-character animation. In ACM SIGGRAPH Asia 2008 papers (SIGGRAPH Asia '08). Association for Computing Machinery, New York, NY, USA, Article 114, 1–8.
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Paper #15: Peng, X. B., Ma, Z., Abbeel, P., Levine, S., & Kanazawa, A. (2021). AMP: Adversarial Motion Priors for Stylized Physics-Based Character Control. arXiv preprint arXiv:2104.02180.
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Camera animation
Paper #15: Jackie Assa, Lior Wolf, Daniel Cohen-Or, The Virtual Director: A Correlation-Based Online Viewing of Human Motion, Computer Graphics Forum, Eurographics 2010
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Paper #16: Thomas Oskam, Robert W. Sumner, Nils Thuerey, and Markus Gross. 2009. Visibility transition planning for dynamic camera control. In Proceedings of the 2009 ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA '09), Dieter Fellner and Stephen Spencer (Eds.). ACM, New York, NY, USA, 55-65. DOI=http://dx.doi.org/10.1145/1599470.1599478
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Paper #17: Boubekeur, Tamy. "ShellCam: Interactive geometry-aware virtual camera control." Image Processing (ICIP), 2014 IEEE International Conference on. IEEE, 2014.
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Paper #18: Rogerio Bonatti, Wenshan Wang, Cherie Ho, Aayush Ahuja, Mirko Gschwindt, Efe Camci, Erdal Kayacan, Sanjiban Choudhury, and Sebastian Scherer. 2019. Autonomous aerial cinematography in unstructured environments with learned artistic decision-making. Journal of Field Robotics (2019).
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Paper #19: Chong Huang, Chuan-En Lin, Zhenyu Yang, Yan Kong, Peng Chen, Xin Yang, and Kwang-Ting Cheng. 2019b. Learning to Film from Professional Human Motion Videos. In Proc. IEEE Int'l Conf. Computer Vision and Pattern Recognition (2019)
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Paper #20: Nägeli, T., Meier, L., Domahidi, A., Alonso-Mora, J., & Hilliges, O. (2017). Real-time planning for automated multi-view drone cinematography. ACM Transactions on Graphics (TOG), 36(4), 1-10.
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Behavioral animation
Paper #21: E. de Sevin, D. Thalmann. A motivational model of action selection for virtual humans. CGI 2005.
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Paper #22:Alexander Shoulson, Max L. Gilbert, Mubbasir Kapadia, Norman I. Badler. An Event-Centric Planning Approach for Dynamic Real-Time Narrative. MIG 2013.
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Paper #23:John Funge, Xiaoyuan Tu, Demetri Terzopoulos. Cognitive Modeling - Knowledge Reasoning and Planning for Intelligent Characters. SIGGRAPH 1999.
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Motion Planning
Paper #24:Strudel, R., Garcia, R., Carpentier, J., Laumond, J. P., Laptev, I., & Schmid, C. (2020). Learning Obstacle Representations for Neural Motion Planning. arXiv preprint arXiv:2008.11174.
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