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.

• Paper [PDF, 6.2 MB]

• Supplemental Document [PDF, 1.0 MB]

• Presentation (PDF, 2.5 MB)

• Code [C++]

• Video (MP4, 58 MB)

@article{karamouzas17,
 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},
}