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

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

Research output: Chapter or section in a book/report/conference proceedingChapter in a published conference proceeding

4 Citations (SciVal)
50 Downloads (Pure)

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 '23
Subtitle of host publicationProceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Place of PublicationNew York, United States
PublisherAssociation for Computing Machinery
Pages1-15
Number of pages15
ISBN (Electronic)9781450394215
DOIs
Publication statusPublished - 19 Apr 2023
Event2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany
Duration: 23 Apr 202328 Apr 2023

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Country/TerritoryGermany
CityHamburg
Period23/04/2328/04/23

Bibliographical note

Funding Information:
This work was supported by the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (Grant No. 101021229, GEMINI: Gaze and Eye Movement in Interaction).

Keywords

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

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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