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

doi:10.1145/3544548.3580685

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 proceeding › Chapter in a published conference proceeding

18 Citations (SciVal)

228 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 language	English
Title of host publication	CHI '23
Subtitle of host publication	Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems
Place of Publication	New York, United States
Publisher	Association for Computing Machinery
Pages	1-15
Number of pages	15
ISBN (Electronic)	9781450394215
DOIs	https://doi.org/10.1145/3544548.3580685
Publication status	Published - 19 Apr 2023
Event	2023 CHI Conference on Human Factors in Computing Systems, CHI 2023 - Hamburg, Germany Duration: 23 Apr 2023 → 28 Apr 2023

Publication series

Name	Conference on Human Factors in Computing Systems - Proceedings

Conference

Conference	2023 CHI Conference on Human Factors in Computing Systems, CHI 2023
Country/Territory	Germany
City	Hamburg
Period	23/04/23 → 28/04/23

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).

Funding

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). 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

Access to Document

10.1145/3544548.3580685

Vergence_Matching
This is the author’s version of the work. The definitive Version of Record was published in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI ’23), April 23–28, 2023, Hamburg, Germany, https://doi.org/10.1145/3544548.3580685.
Accepted author manuscript, 9.92 MBLicence: CC BY

Cite this

Sidenmark, L., Clarke, C., Newn, J., Lystbæk, M. N., Pfeuffer, K., & Gellersen, H. (2023). Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. In CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-15). Article 257 (Conference on Human Factors in Computing Systems - Proceedings). Association for Computing Machinery. https://doi.org/10.1145/3544548.3580685

Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. / Sidenmark, Ludwig; Clarke, Christopher; Newn, Joshua et al.
CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, United States: Association for Computing Machinery, 2023. p. 1-15 257 (Conference on Human Factors in Computing Systems - Proceedings).

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

Sidenmark, L, Clarke, C, Newn, J, Lystbæk, MN, Pfeuffer, K & Gellersen, H 2023, Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. in CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems., 257, Conference on Human Factors in Computing Systems - Proceedings, Association for Computing Machinery, New York, United States, pp. 1-15, 2023 CHI Conference on Human Factors in Computing Systems, CHI 2023, Hamburg, Germany, 23/04/23. https://doi.org/10.1145/3544548.3580685

Sidenmark L, Clarke C, Newn J, Lystbæk MN, Pfeuffer K, Gellersen H. Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. In CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, United States: Association for Computing Machinery. 2023. p. 1-15. 257. (Conference on Human Factors in Computing Systems - Proceedings). doi: 10.1145/3544548.3580685

Sidenmark, Ludwig ; Clarke, Christopher ; Newn, Joshua et al. / Vergence Matching : Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. CHI '23: Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. New York, United States : Association for Computing Machinery, 2023. pp. 1-15 (Conference on Human Factors in Computing Systems - Proceedings).

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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.",

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

Abstract

Publication series

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Bibliographical note

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Keywords

ASJC Scopus subject areas

Access to Document

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