With or Without You: Effect of Contextual and Responsive Crowds on VR-based Crowd Motion Capture

While data is vital to better understand and model interactions within human crowds, capturing real crowd motions is extremely challenging. Virtual Reality (VR) demonstrated its potential to help, by immersing users into either simulated virtual crowds based on autonomous agents, or within motion-capture-based crowds. In the latter case, users' own captured motion can be used to progressively extend the size of the crowd, a paradigm called Record-and-Replay (2R). However, both approaches demonstrated several limitations which impact the quality of the acquired crowd data. In this paper, we propose the new concept of contextual crowds to leverage both crowd simulation and the 2R paradigm towards more consistent crowd data. We evaluate two different strategies to implement it, namely a Replace-Record-Replay (3R) paradigm where users are initially immersed into a simulated crowd whose agents are successively replaced by the user's captured-data, and a Replace-Record-Replay-Responsive (4R) paradigm where the pre-recorded agents are additionally endowed with responsive capabilities. These two paradigms are evaluated through two real-world-based scenarios replicated in VR. Our results suggest that the behaviors observed in VR users with surrounding agents from the beginning of the recording process are made much more natural, enabling 3R or 4R paradigms to improve the consistency of captured crowd datasets.

1 Introduction

Crowd motion data plays a critical role in understanding and modeling crowd dynamics and local pedestrian behaviors. While comprehensive datasets that include body movements, eye gaze direction, and hand gestures would be valuable, most of the existing crowd datasets primarily document pedestrian positional data, due to technological constraints [22]. However, with the emergence of data-driven crowd modeling approaches, the need for rich and large datasets is paramount. As traditional approaches struggle to find a good compromise between data reconstruction and time-money cost efficiency, Virtual Reality (VR) has recently been explored as a data collection tool [3], [8], [48]–. Indeed, performing data acquisition in an immersive virtual environ- ment allows for improved scene control and repeatability, a wider range of acquired modalities, as well as more efficient data processing with guaranteed synchronization.