Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection


Gaze pointing is the de facto standard to infer attention and interact in 3D environments but is limited by motor and sensor limitations. To circumvent these limitations, we propose a vergence-based motion correlation method to detect visual attention toward very small targets. Smooth depth movements relative to the user are induced on 3D objects, which cause slow vergence eye movements when looked upon. Using the principle of motion correlation, the depth movements of the object and vergence eye movements are matched to determine which object the user is focussing on. In two user studies, we demonstrate how the technique can reliably infer gaze attention on very small targets, systematically explore how different stimulus motions affect attention detection, and show how the technique can be extended to multi-target selection. Finally, we provide example applications using the concept and design guidelines for small target and accuracy-independent attention detection in 3D environments.

Ludwig Sidenmark
Lancaster University, Lancaster, United Kingdom
Christopher Clarke
University of Bath, Bath, United Kingdom
Joshua Newn
Lancaster University, Lancaster, Lancashire, United Kingdom
Mathias N.. Lystbæk
Aarhus University, Aarhus, Denmark
Ken Pfeuffer
Aarhus University, Aarhus, Denmark
Hans Gellersen
Lancaster University, Lancaster, United Kingdom


会議: CHI 2023

The ACM CHI Conference on Human Factors in Computing Systems (

セッション: Eye Gaze and New Body

Hall E
6 件の発表
2023-04-25 23:30:00
2023-04-26 00:55:00