Position-Based Multi-Agent Dynamics for Real-Time Crowd Simulation (MiG paper)

TL;DR

This paper introduces a novel position-based multi-agent simulation method that efficiently models detailed agent behaviors and collisions in dense crowds at interactive rates, suitable for real-time applications like games.

Contribution

The paper presents a new crowd simulation technique using Position-Based Dynamics that handles large agent populations with detailed collision avoidance and collective behavior modeling.

Findings

Runs at interactive rates for hundreds of thousands of agents

Accurately models short- and long-range collision avoidance

Supports detailed collective behaviors in dense crowds

Abstract

Exploiting the efficiency and stability of Position-Based Dynamics (PBD), we introduce a novel crowd simulation method that runs at interactive rates for hundreds of thousands of agents. Our method enables the detailed modeling of per-agent behavior in a Lagrangian formulation. We model short-range and long-range collision avoidance to simulate both sparse and dense crowds. On the particles representing agents, we formulate a set of positional constraints that can be readily integrated into a standard PBD solver. We augment the tentative particle motions with planning velocities to determine the preferred velocities of agents, and project the positions onto the constraint manifold to eliminate colliding configurations. The local short-range interaction is represented with collision and frictional contact between agents, as in the discrete simulation of granular materials. We incorporate a cohesion model for modeling collective behaviors and propose a new constraint for dealing with potential future collisions. Our new method is suitable for use in interactive games.