Learning motion rules from real data: Neural network for crowd simulation

This paper addresses the problem of efficiently simulating a believable virtual crowd. Our method is the first one that uses the Neural Network (NN) model to fit behaviors from real crowd data to a crowd simulation. Unlike several rule-based approaches that often result in 'walking robots', our model can learn motion rules derived from real data and later simulate human walking motions. Additionally, unlike the existing data-driven crowd simulation methods that have to perform search operations on the bound dataset simultaneously during the simulation, our model directly uses the NN model to generate the proper motion for each crowd member. The proposed method is being tested on various scenarios and compared with state-of-the-art state-action-based methods that are commonly employed in data-driven crowd simulation systems. The results demonstrate a significant increase in speed, as well as better simulation quality.