The One-Man-Crowd: Single User Generation of Crowd Motions Using Virtual Reality

Crowd motion data is fundamental for understanding and simulating realistic crowd behaviours. Such data is usually collected through controlled experiments to ensure that both desired individual interactions and collective behaviours can be observed. It is however scarce, due to ethical concerns and logistical difficulties involved in its gathering, and only covers a few typical crowd scenarios. In this work, we propose and evaluate a novel Virtual Reality based approach lifting the limitations of real-world experiments for the acquisition of crowd motion data. Our approach immerses a single user in virtual scenarios where he/she successively acts each crowd member. By recording the past trajectories and body movements of the user, and displaying them on virtual characters, the user progressively builds the overall crowd behaviour by him/herself. We validate the feasibility of our approach by replicating three real experiments, and compare both the resulting emergent phenomena and the individual interactions to existing real datasets. Our results suggest that realistic collective behaviours can naturally emerge from virtual crowd data generated using our approach, even though the variety in behaviours is lower than in real situations. These results provide valuable insights to the building of virtual crowd experiences, and reveal key directions for further improvements.

1 Introduction

Crowd datasets, i.e., recordings of trajectories from numerous people moving together in a same location, are paramount in the understanding, modelling and simulation of crowd behaviours. For instance, these datasets are used to study and understand collective behaviours, or to train, calibrate and evaluate simulation models. Such datasets remain however rare in spite of the large interest they yield. This scarcity is due to various reasons, such as costs, logistical, ethical and technical issues. One can imagine the effort it takes to bring many participants in a large enough laboratory equipped with scaled tracking technologies, or, out of labs, the technical difficulties in tracking people in crowded public places, after obtaining the required authorisations. The current lack of valuable crowd datasets is therefore significantly hampering research on understanding and simulating collective behaviours. Fig. 1.

Snapshot of crowd motions generated using the one-man-crowd approach. A single user successively embodies each displayed virtual agent in the order indicated by the highlighting colour (from blue to yellow). We studied 3 scenarios that replicated existing experiments from left to right: Circular unidirectional flow, bottleneck situation, inflow behaviour (entering a lift).