Implicit Crowds:
Optimization Integrator for Robust Crowd Simulation

Ioannis Karamouzas1, Nick Sohre2, Rahul Narain2, Stephen J. Guy2
1Clemson University, 2University of Minnesota
In ACM Transactions on Graphics (Proceedings of SIGGRAPH 2017)


Large multi-agent systems such as crowds involve inter-agent interactions that are typically anticipatory in nature, depending strongly on both the positions and the velocities of agents. We show how the nonlinear, anticipatory forces seen in multi-agent systems can be made compatible with recent work on energy-based formulations in physics-based animation, and propose a simple and effective optimization-based integration scheme for implicit integration of such systems. We apply this approach to crowd simulation by using a state-of-the-art model derived from a recent analysis of human crowd data, and adapting it to our framework. Our approach provides, for the first time, guaranteed collision-free motion while simultaneously maintaining high-quality collective behavior in a way that is insensitive to simulation parameters such as time step size and crowd density. These benefits are demonstrated through simulation results on various challenging scenarios and validation against real-world crowd data.




 author = {Karamouzas, Ioannis and Sohre, Nick and Narain, Rahul and Guy, Stephen J.},
 title = {Implicit Crowds: Optimization Integrator for Robust Crowd Simulation},
 journal = {ACM Transactions on Graphics},
 volume = {36},
 number = {4},
 year = {2017},
 month = jul,
 doi = {10.1145/3072959.3073705},
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