Introducing Anisotropic Fields for Enhanced Diversity in Crowd Simulation

TL;DR

This paper introduces anisotropic fields (AFs) to enhance behavioral diversity in crowd simulations, enabling more realistic and varied emergent crowd behaviors by using intuitive sketching or real video data.

Contribution

The authors propose a novel AF-based framework that significantly improves behavioral diversity in crowd simulations compared to traditional methods.

Findings

Enhanced behavioral diversity in simulated crowds

Higher similarity to real-world crowd behaviors

Flexible AF generation from sketches or videos

Abstract

Large crowds exhibit intricate behaviors and significant emergent properties, yet existing crowd simulation systems often lack behavioral diversity, resulting in homogeneous simulation outcomes. To address this limitation, we propose incorporating anisotropic fields (AFs) as a fundamental structure for depicting the uncertainty in crowd movement. By leveraging AFs, our method can rapidly generate crowd simulations with intricate behavioral patterns that better reflect the inherent complexity of real crowds. The AFs are generated either through intuitive sketching or extracted from real crowd videos, enabling flexible and efficient crowd simulation systems. We demonstrate the effectiveness of our approach through several representative scenarios, showcasing a significant improvement in behavioral diversity compared to classical methods. Our findings indicate that by incorporating AFs, crowd simulation systems can achieve a much higher similarity to real-world crowd systems. Our code is publicly available at https://github.com/tomblack2014/AF\_Generation.