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

arXiv: https://arxiv.org/abs/2306.03381 · HTML: https://mlsanity.com/paper/2306.03381/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 72 references that are in the map.
- First posted on arXiv 6 Jun 2023.

## Builds on (most cited first, 10 of 10)

- [SlowFast Networks for Video Recognition](https://mlsanity.com/paper/1812.03982/) (2018)
- [VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://mlsanity.com/paper/2203.12602/) (2022)
- [Depth Map Prediction from a Single Image using a Multi-Scale Deep Network](https://mlsanity.com/paper/1406.2283/) (2014)
- [FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks](https://mlsanity.com/paper/1612.01925/) (2016)
- [Optical Flow Estimation using a Spatial Pyramid Network](https://mlsanity.com/paper/1611.00850/) (2016)
- [Enforcing geometric constraints of virtual normal for depth prediction](https://mlsanity.com/paper/1907.12209/) (2019)
- [Efficiently Identifying Task Groupings for Multi-Task Learning](https://mlsanity.com/paper/2109.04617/) (2021)
- [PyTorchVideo: A Deep Learning Library for Video Understanding](https://mlsanity.com/paper/2111.09887/) (2021)
- [Machine learning architectures to predict motion sickness using a Virtual Reality rollercoaster simulation tool](https://mlsanity.com/paper/1811.01106/) (2018)
- [Towards a Better Understanding of VR Sickness: Physical Symptom Prediction for VR Contents](https://mlsanity.com/paper/2104.06780/) (2021)

## Built on by (most cited first, 6 of 6)

- [Mazed and Confused: A Dataset of Cybersickness, Working Memory, Mental Load, Physical Load, and Attention During a Real Walking Task in VR](https://mlsanity.com/paper/2409.06898/) (2024)
- [Motion-Based User Identification across XR and Metaverse Applications by Deep Classification and Similarity Learning](https://mlsanity.com/paper/2509.08539/) (2025)
- [XRZoo: A Large-Scale and Versatile Dataset of Extended Reality (XR) Applications](https://mlsanity.com/paper/2412.06759/) (2024)
- [Combine Virtual Reality and Machine-Learning to Identify the Presence of Dyslexia: A Cross-Linguistic Approach](https://mlsanity.com/paper/2509.04510/) (2025)
- [Towards Cybersickness Severity Classification from VR Gameplay Videos Using Transfer Learning and Temporal Modeling](https://mlsanity.com/paper/2510.10422/) (2025)
- [VRScout: Towards Real-Time, Autonomous Testing of Virtual Reality Games](https://mlsanity.com/paper/2511.00002/) (2025)

## Abstract (arXiv)

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.