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Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review
Research output: Contribution to conference - Without ISBN/ISSN › Conference paper › peer-review
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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 -