ORCANet: Differentiable multi-parameter learning for crowd simulation

Realistic crowd simulation has always been an important research field in computer graphics. While both agent-based motion models and data-driven behavior models have made some progress, they are still suffering from either huge effort of multi-parameter tuning or limited realistic motion. In this article, we propose a novel and differentiable multi-parameter learning method for crowd simulation, which is called ORCANet. The main idea is to learn from real data and inverse evaluating the multi-parameter for subsequent simulation. ORCANet uses classic optimal reciprocal collision avoidance (ORCA) as a basic motion model which is integrated into the deep learning framework. Addressing the feature of linear programming and non-differentiable operation, a Gaussian kernel is added to approximate the role of neighbor distance in collision avoidance, which turns the original discrete operation into a fully differentiable forward simulation. Furthermore, we leverage ORCANet to optimize the multi-parameter combination in synthetic and real-world datasets. ORCANet is proved to rapidly converge to correct parameter values and regenerate the input synthetic sequence. Moreover, experiments on real-world datasets by the metric of pedestrian trajectories verified that a more realistic crowd simulation has been generated through ORCANet.