  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 `VR.net', 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 VR.net
through several applications, such as risk factor detection and sickness level
prediction. We continuously expand VR.net and envision its next version
offering 10X more data than the current form. We believe that the scale,
accuracy, and diversity of VR.net can offer unparalleled opportunities for VR
motion sickness research and beyond.
