We present a GPU-friendly path-planning framework for largescale crowd simulation (Figure 1). This framework has been used to simulate 65,000 agents at real-time framerates on a single commodity GPU. By combining a continuum-based global path planner with a fine-grained avoidance model, we can perform expensive global planning at a coarse resolution and lower update rate while the local avoidan...

We present a data-driven method for the real-time synthesis of believable steering behaviors for virtual crowds. The proposed method interlinks the input examples into a structure we call the Perception–Action Graph (PAG) which can be used at run-time to efficiently synthesize believable virtual crowds. A virtual character’s state is encoded using a temporal representation, the Temporal Percept...

We address the problem of simulating pedestrian crowd behaviors in real time. To date, two approaches can be used in modeling and simulation of crowd behaviors, i.e. macroscopic and microscopic models. Microscopic simulation techniques can capture the accuracy simulation of individualistic pedestrian behavior while macroscopic simulations maximize the efficiency of simulation; neither of them a...

Crowd congestion causes accidents and sometimes leads to hundreds of injuries deaths. To mitigate crowd congestion, we could design a space where the pedestrians can move smoothly, or intervene control movement. Recently, computer simulation is often used determine optimal spatial control. The methods movement are well organized because it has been an active area research since 1990s understand...

Crowd-labeling emerged from the need to label large-scale and complex data, a tedious, expensive, and time-consuming task. But the problem of obtaining good quality labels from a crowd and their integration is still unresolved. To address this challenge, we propose a new framework that automatically combines and boosts bulk crowd labels supported by limited number of “ground truth” labels from ...