Tomer Weiss
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We present a novel method for simulating groups moving in formation. Recent approaches for simulating group motion operate via forces or velocity-connections. While such approaches are effective for several cases, they do not easily scale to large crowds, irregular formation shapes, and they provide limited fine-grain control over agent and group behaviors. In this paper we propose a novel approach that addresses these difficulties via positional constraints, with a position-based dynamics solver. Our approach allows real-time, interactive simulation of a variety of group numbers, formation shapes, and scenarios of up to thousands of agents.
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- Beatriz Cabrero-Daniel, Ricardo Marques, Ludovic Hoyet, Julien Pettré, and Josep Blat. 2022. Dynamic Combination of Crowd Steering Policies Based on Context. In Computer Graphics Forum, Vol. 41. Wiley Online Library, 209--219.Google Scholar
- David F Crouse. 2016. On implementing 2D rectangular assignment algorithms. IEEE Trans. Aerospace and Electronic Systems 52, 4 (2016), 1679--1696.Google ScholarCross Ref
- Paolo Fiorini and Zvi Shiller. 1998. Motion planning in dynamic environments using velocity obstacles. International J. of Robotics Research 17, 7 (1998), 760--772.Google ScholarCross Ref
- Stephen J Guy, Jatin Chhugani, Changkyu Kim, Nadathur Satish, Ming Lin, Dinesh Manocha, and Pradeep Dubey. 2009. Clearpath: highly parallel collision avoidance for multi-agent simulation. In ACM SIGGRAPH/Eurographics Symposium on Computer Animation (SCA). ACM, 177--187.Google ScholarDigital Library
- Dirk Helbing, Illés Farkas, and Tamas Vicsek. 2000. Simulating dynamical features of escape panic. Nature 407, 6803 (2000), 487--490.Google ScholarCross Ref
- Leroy F Henderson. 1974. On the fluid mechanics of human crowd motion. Transportation Research 8, 6 (1974), 509--515.Google ScholarCross Ref
- Joseph Henry, Hubert PH Shum, and Taku Komura. 2013. Interactive formation control in complex environments. IEEE Trans. Visualization and Computer Graphics 20, 2 (2013), 211--222.Google ScholarDigital Library
- Yuanming Hu. 2021. The Taichi High-Performance and Differentiable Programming Language for Sparse and Quantized Visual Computing. Ph.D. Dissertation. Massachusetts Institute of Technology.Google Scholar
- Roger L Hughes. 2002. A continuum theory for the flow of pedestrians. Transportation Research Part B: Methodological 36, 6 (2002), 507--535.Google ScholarCross Ref
- Ben Jones, Nils Thuerey, Tamar Shinar, and Adam W Bargteil. 2016. Example-based plastic deformation of rigid bodies. ACM Trans. Graphics (TOG) 35, 4 (2016), 1--11.Google ScholarDigital Library
- Alain Juarez-Perez and Marcelo Kallmann. 2018. Fast behavioral locomotion with layered navigation meshes. In Symposium on Interactive 3D Graphics and Games (I3D). 1--6.Google ScholarDigital Library
- Ioannis Karamouzas and Stephen J Guy. 2015. Prioritized group navigation with formation velocity obstacles. In International Conference on Robotics and Automation (ICRA). IEEE, 5983--5989.Google ScholarCross Ref
- Ioannis Karamouzas and Mark Overmars. 2011. Simulating and evaluating the local behavior of small pedestrian groups. IEEE Trans. Visualization and Computer Graphics 18, 3 (2011), 394--406.Google ScholarDigital Library
- Ioannis Karamouzas, Brian Skinner, and Stephen J Guy. 2014. Universal power law governing pedestrian interactions. Physical review letters 113, 23 (2014), 238701.Google Scholar
- Yihao Li, Tianyu Huang, Yifan Liu, Xiaojun Chang, and Gangyi Ding. 2022. Anchor-based crowd formation transformation. Computer Animation and Virtual Worlds 33, 5 (2022), e2111.Google ScholarCross Ref
- Miles Macklin, Matthias Müller, Nuttapong Chentanez, and Tae-Yong Kim. 2014. Unified particle physics for real-time applications. ACM Trans. Graphics (TOG) 33, 4 (2014), 1--12.Google ScholarDigital Library
- Mehdi Moussaid, Niriaska Perozo, Simon Garnier, Dirk Helbing, and Guy Theraulaz. 2010. The walking behaviour of pedestrian social groups and its impact on crowd dynamics. PloS one 5, 4 (2010), e10047.Google ScholarCross Ref
- Matthias Müller, Bruno Heidelberger, Matthias Teschner, and Markus Gross. 2005. Meshless deformations based on shapeGoogle Scholar
- matching. ACM Trans. Graphics (TOG) 24, 3 (2005), 471--478.Google Scholar
- M. Müller, B. Heidelberger, M. Hennix, and J. Ratcliff. 2007. Position based dynamics. In J. Vis. Comun. Image Represent. 109--118.Google Scholar
- Andreas Panayiotou, Theodoros Kyriakou, Marilena Lemonari, Yiorgos Chrysanthou, and Panayiotis Charalambous. 2022. CCP: Configurable Crowd Profiles. ACM Trans. Graphics (TOG) (2022).Google Scholar
- N. Pelechano, J.M. Allbeck, M. Kapadia, and N.I. Badler. 2016. Simulating Heterogeneous Crowds with Interactive Behaviors. CRC Press.Google Scholar
- Zhiguo Ren, Panayiotis Charalambous, Julien Bruneau, Qunsheng Peng, and Julien Pettré. 2017. Group Modeling: A Unified Velocity-Based Approach. In Computer Graphics Forum, Vol. 36. Wiley Online Library, Wiley, 45--56.Google Scholar
- Craig W Reynolds. 1987. Flocks, herds and schools: A distributed behavioral model. In Proceedings of the 14th annual conference on Computer graphics and Interactive techniques. 25--34.Google ScholarDigital Library
- Craig W Reynolds et al. 1999. Steering behaviors for autonomous characters. In Game Developers Conference, Vol. 1999. Citeseer, 763--782.Google Scholar
- Alec R Rivers and Doug L James. 2007. FastLSM: fast lattice shape matching for robust real-time deformation. ACM Trans. Graphics (TOG) 26, 3 (2007), 82--es.Google ScholarDigital Library
- Otger Rogla, Gustavo A Patow, and Nuria Pelechano. 2021. Procedural crowd generation for semantically augmented virtual cities. Computers & Graphics 99 (2021), 83--99.Google ScholarDigital Library
- Wei Shao and Demetri Terzopoulos. 2007. Autonomous pedestrians. Graphical models 69, 5-6 (2007), 246--274.Google Scholar
- Ritesh Sharma, Tomer Weiss, and Marcelo Kallmann. 2020. Plane-Based Local Behaviors for Multi-Agent 3D Simulations with Position-Based Dynamics. In IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). IEEE, 214--217.Google ScholarCross Ref
- Adrien Treuille, Seth Cooper, and Zoran Popović. 2006. Continuum crowds. ACM Trans. Graphics (TOG) 25, 3 (2006), 1160--1168.Google ScholarDigital Library
- Unity. 2022. Unity - Game Engine. https://unity3d.com/.Google Scholar
- Wouter van Toll, Fabien Grzeskowiak, Axel López Gandía, Javad Amirian, Florian Berton, Julien Bruneau, Beatriz Cabrero Daniel, Alberto Jovane, and Julien Pettré. 2020. Generalized microscropic crowd simulation using costs in velocity space. In Symposium on Interactive 3D Graphics and Games (I3D). 1--9.Google ScholarDigital Library
- Pauli Virtanen, Ralf Gommers, Travis E Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, Evgeni Burovski, Pearu Peterson, Warren Weckesser, Jonathan Bright, et al. 2020. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nature methods 17, 3 (2020), 261--272.Google Scholar
- Tomer Weiss. 2019. Implementing Position-Based Real-Time Simulation of Large Crowds. In IEEE International Conference on Artificial Intelligence and Virtual Reality (AIVR). IEEE, 306--3061.Google Scholar
- Tomer Weiss, Chenfanfu Jiang, Alan Litteneker, and Demetri Terzopoulos. 2017. Position-based multi-agent dynamics for real-time crowd simulation. In Proceedings of the 10th International Conference on Motion in Games. 1--8.Google ScholarDigital Library
- Tomer Weiss, Alan Litteneker, Chenfanfu Jiang, and Demetri Terzopoulos. 2019. Position-based real-time simulation of large crowds. Computers & Graphics 78 (2019), 12--22.Google ScholarCross Ref
- Mingliang Xu, Yunpeng Wu, Yangdong Ye, Illes Farkas, Hao Jiang, and Zhigang Deng. 2015. Collective Crowd Formation Transform with Mutual Information-Based Runtime Feedback. Computer Graphics Forum 34, 1 (2015), 60--73.Google ScholarDigital Library
- Xinyu Zheng, Chen Zong, Jingliang Cheng, Jian Xu, Shiqing Xin, Changhe Tu, Shuangmin Chen, and Wenping Wang. 2021. Visually smooth multi-UAV formation transformation. Graphical Models 116 (2021), 101111.Google ScholarCross Ref
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