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

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

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Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. / Sidenmark, Ludwig; Clarke, Christopher; Newn, Joshua et al.
2023. 257:1-257:15 Paper presented at 2023 ACM CHI Conference on Human Factors in Computing Systems, Hamburg, Hamburg, Germany.

Research output: Contribution to conference - Without ISBN/ISSN Conference paperpeer-review

Harvard

Sidenmark, L, Clarke, C, Newn, J, Lystbæk, M, Pfeuffer, K & Gellersen, H 2023, 'Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection', Paper presented at 2023 ACM CHI Conference on Human Factors in Computing Systems, Hamburg, Germany, 23/04/23 - 28/04/23 pp. 257:1-257:15. https://doi.org/10.1145/3544548.3580685

APA

Sidenmark, L., Clarke, C., Newn, J., Lystbæk, M., Pfeuffer, K., & Gellersen, H. (2023). Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. 257:1-257:15. Paper presented at 2023 ACM CHI Conference on Human Factors in Computing Systems, Hamburg, Hamburg, Germany. https://doi.org/10.1145/3544548.3580685

Vancouver

Sidenmark L, Clarke C, Newn J, Lystbæk M, Pfeuffer K, Gellersen H. Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. 2023. Paper presented at 2023 ACM CHI Conference on Human Factors in Computing Systems, Hamburg, Hamburg, Germany. doi: 10.1145/3544548.3580685

Author

Sidenmark, Ludwig ; Clarke, Christopher ; Newn, Joshua et al. / Vergence Matching : Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection. Paper presented at 2023 ACM CHI Conference on Human Factors in Computing Systems, Hamburg, Hamburg, Germany.15 p.

Bibtex

@conference{6abce0a833c2448d9708636f9f91dd99,
title = "Vergence Matching: Inferring Attention to Objects in 3D Environments for Gaze-Assisted Selection",
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.",
keywords = "Selection, Attention Detection, Virtual Reality, Gaze, Vergence, Motion Correlation, Small Targets",
author = "Ludwig Sidenmark and Christopher Clarke and Joshua Newn and Mathias Lystb{\ae}k and Ken Pfeuffer and Hans Gellersen",
year = "2023",
month = apr,
day = "19",
doi = "10.1145/3544548.3580685",
language = "English",
pages = "257:1--257:15",
note = "2023 ACM CHI Conference on Human Factors in Computing Systems, CHI 2023 ; Conference date: 23-04-2023 Through 28-04-2023",
url = "https://chi2023.acm.org/",

}

RIS

TY - CONF

T1 - Vergence Matching

T2 - 2023 ACM CHI Conference on Human Factors in Computing Systems

AU - Sidenmark, Ludwig

AU - Clarke, Christopher

AU - Newn, Joshua

AU - Lystbæk, Mathias

AU - Pfeuffer, Ken

AU - Gellersen, Hans

PY - 2023/4/19

Y1 - 2023/4/19

N2 - 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.

AB - 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.

KW - Selection

KW - Attention Detection

KW - Virtual Reality

KW - Gaze

KW - Vergence

KW - Motion Correlation

KW - Small Targets

U2 - 10.1145/3544548.3580685

DO - 10.1145/3544548.3580685

M3 - Conference paper

SP - 257:1-257:15

Y2 - 23 April 2023 through 28 April 2023

ER -