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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    Title :

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


    Contributors:

    Publication date :

    2023-04-19


    Remarks:

    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 . , 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



    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    629



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