VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research

PubDate: Jun 2023

Teams: The University of Auckland;National University of Singapore;Meta

Writers: Elliott Wen, Chitralekha Gupta, Prasanth Sasikumar, Mark Billinghurst, James Wilmott, Emily Skow, Arindam Dey, Suranga Nanayakkara

PDF: VR.net: A Real-world Dataset for Virtual Reality Motion Sickness Research

Abstract

Researchers have used machine learning approaches to identify motion sickness in VR experience. These approaches demand an accurately-labeled, real-world, and diverse dataset for high accuracy and generalizability. As a starting point to address this need, we introduce `this http URL’, a dataset offering approximately 12-hour gameplay videos from ten real-world games in 10 diverse genres. For each video frame, a rich set of motion sickness-related labels, such as camera/object movement, depth field, and motion flow, are accurately assigned. Building such a dataset is challenging since manual labeling would require an infeasible amount of time. Instead, we utilize a tool to automatically and precisely extract ground truth data from 3D engines’ rendering pipelines without accessing VR games’ source code. We illustrate the utility of this http URL through several applications, such as risk factor detection and sickness level prediction. We continuously expand this http URL and envision its next version offering 10X more data than the current form. We believe that the scale, accuracy, and diversity of this http URL can offer unparalleled opportunities for VR motion sickness research and beyond.