Evaluation of Eye Tracking Signal Quality for Virtual Reality Applications: A Case Study in the Meta Quest Pro

TL;DR

This study tackles assessing eye-tracking signal quality in VR by evaluating the Meta Quest Pro across 78 participants, reporting spatial accuracy, spatial precision, and linearity under controlled luminance and slippage conditions. It introduces a practical, user-centric evaluation framework using user percentiles (U) and error percentiles within users (E), enabling design guidance that accounts for both population-wide coverage and individual variability. Key findings show the device is largely robust to luminance changes but sensitive to headset slippage, especially for high-percentile users, highlighting the need for slippage-robust gaze estimation and interface designs that accommodate worst-case performance. Overall, the work provides concrete design implications for gaze-based interaction and foveated rendering in VR, improving usability and accessibility across a broad user base.

Abstract

We present an extensive, in-depth analysis of the eye tracking capabilities of the Meta Quest Pro virtual reality headset using a dataset of eye movement recordings collected from 78 participants. In addition to presenting classical signal quality metrics--spatial accuracy, spatial precision and linearity--in ideal settings, we also study the impact of background luminance and headset slippage on device performance. We additionally present a user-centered analysis of eye tracking signal quality, where we highlight the potential differences in user experience as a function of device performance. This work contributes to a growing understanding of eye tracking signal quality in virtual reality headsets, where the performance of applications such as gaze-based interaction, foveated rendering, and social gaze are directly dependent on the quality of eye tracking signal.

Figures (8)

• Figure 1: The orientation of the targets during the random saccades tasks, including the colors employed for RAN 127 and RAN 63.
• Figure 2: The distribution of spatial accuracy calculated across both luminance conditions. Each data point represents E50 (left) and E95 (right) mean spatial accuracy from a user. The white circle represents the mean spatial accuracy across the entire population.
• Figure 3: The spatial accuracy requirements to accommodate different proportions of the user population in both the bright (left) and dark (right) background random saccades tasks. The E50, E75, and E95 metrics are presented for all user percentiles shown here.
• Figure 4: The distribution of sample-to-sample (S2S) RMS spatial precision across both luminance conditions. Data points represent E50 (left) and E95 (right) of mean spatial precision from a user. The white circle denotes the mean spatial precision across the population.
• Figure 5: Violin plots comparing spatial accuracy before and after the brow task (\ref{['fig:accuracy-slippage-before-after']}) and during the brow task (\ref{['fig:accuracy-slippage-intra-task']}). Each data point represents a single mean from each user. The white circle represents mean spatial accuracy across the population.