Bilas Talukdar

Abstract

In crowd simulation algorithms, discs are commonly used due to their geometrical simplicity. However, discs overestimate the body’s projected shape on the ground plane, and do not naturally embody rotational information for each crowd agent. We propose a crowd simulation algorithm that represents crowd agents as 2D capsules. Our algorithm is based on position-based dynamics (PBD), for which we design novel constraints for capturing dynamics for capsule-shaped agents. We demonstrate our algorithm in several scenarios, showing superior performance compared to a disc-based representation on settings that range from sparse to dense.


Below we list the key contributions of our work:

We represent each crowd agent with a torso, which realistically captures a pedestrian’s rotation and shape. This enables simulating real-world dynamic details that are difficult to capture with discs. Our approach works in settings ranging from sparse to dense.

Our method makes no assumption about the environment or specific agent active-passive behavior configurations.

We present novel torso agent-agent collision avoidance constraints by extending the position-based crowds formulation.

Our method generates dynamic crowds with varied movement and rotation behaviors, which respond to environment spacing, and mimics rotational responses observed in real-world crowds.

Supplementary Video