The Capturing and Logging Ecological Virtual Experiences and Reality (CLEVER) - Job Simulator Dataset

2026-08-13

AI Summary

This study addresses the scarcity of publicly available resources for VR motion tracking and interaction data by constructing an open-source dataset derived from Job Simulator with 95 participants. The authors systematically review existing datasets and detail an ecologically valid data collection and annotation pipeline based on SteamVR, comprehensively describing the dataset’s attributes. Furthermore, the work validates the dataset's utility through machine learning tasks, including user identification and attribute prediction. By releasing this high-quality benchmark, the research fills a critical gap in the field, providing essential data support and an experimental foundation for advancing VR behavior analysis, user modeling, and related algorithmic research.

Abstract

Virtual reality (VR) motion tracking and interaction data has become increasingly recognized as valuable for machine learning experiments for a variety of purposes, including predicting user identities, predicting user attributes like gender and age, predicting retention and learning, and more. However, there exist a limited number of publicly accessible VR motion datasets. In this paper, we present a new open-source dataset of 95 participants playing the SteamVR game Job Simulator. Additionally, we review existing datasets, detail our study procedure, describe our data collection process, list attributes of our dataset, and suggest future work, impact, and applications.

Problem

• Virtual Reality
• Motion Tracking
• Dataset
• Machine Learning

Innovation

• VR motion dataset
• open-source dataset
• Job Simulator
• motion tracking
• machine learning