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

Ludwig Sidenmark, Christopher Clarke, Joshua Newn, Mathias Nørhede Lystbæk, Ken Pfeuffer, Hans Gellersen

Research output: Contribution to book/anthology/report/proceedingArticle in proceedingsResearchpeer-review

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Abstract

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.
Original languageEnglish
Title of host publicationCHI 2023 - Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Number of pages15
PublisherAssociation for Computing Machinery
Article number257
ISBN (Electronic)9781450394215
DOIs
Publication statusAccepted/In press - 1 Mar 2023

Keywords

  • Attention Detection
  • Gaze
  • Motion Correlation
  • Selection
  • Small Targets
  • Vergence
  • Virtual Reality

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