Practical Saccade Prediction for Head-Mounted Displays: Towards a Comprehensive Model

TL;DR

This paper investigates factors influencing saccade prediction accuracy in VR/AR, proposing a simple correction method to improve gaze extrapolation for reducing latency artifacts in foveated rendering.

Contribution

It introduces a comprehensive analysis of factors affecting saccade prediction and presents an efficient correction technique adaptable to existing methods.

Findings

Saccade orientation significantly impacts prediction accuracy.

Smooth pursuit eye movements influence saccade prediction.

The proposed correction method improves prediction without extensive data collection.

Abstract

Eye-tracking technology is an integral component of new display devices such as virtual and augmented reality headsets. Applications of gaze information range from new interaction techniques exploiting eye patterns to gaze-contingent digital content creation. However, system latency is still a significant issue in many of these applications because it breaks the synchronization between the current and measured gaze positions. Consequently, it may lead to unwanted visual artifacts and degradation of user experience. In this work, we focus on foveated rendering applications where the quality of an image is reduced towards the periphery for computational savings. In foveated rendering, the presence of latency leads to delayed updates to the rendered frame, making the quality degradation visible to the user. To address this issue and to combat system latency, recent work proposes to use saccade landing position prediction to extrapolate the gaze information from delayed eye-tracking samples. While the benefits of such a strategy have already been demonstrated, the solutions range from simple and efficient ones, which make several assumptions about the saccadic eye movements, to more complex and costly ones, which use machine learning techniques. Yet, it is unclear to what extent the prediction can benefit from accounting for additional factors. This paper presents a series of experiments investigating the importance of different factors for saccades prediction in common virtual and augmented reality applications. In particular, we investigate the effects of saccade orientation in 3D space and smooth pursuit eye-motion (SPEM) and how their influence compares to the variability across users. We also present a simple yet efficient correction method that adapts the existing saccade prediction methods to handle these factors without performing extensive data collection.