Exploring 3D Interaction with Gaze Guidance in Augmented Reality
Yiwei Bao, Jiaxi Wang, Zhimin Wang, Feng Lu
Abstract
Recent research based on hand-eye coordination has shown that gaze could improve object selection and translation experience under certain scenarios in AR. However, several limitations still exist. Specifically, we investigate whether gaze could help object selection with heavy 3D occlusions and help 3D object translation in the depth dimension. In addition, we also investigate the possibility of reducing the gaze calibration burden before use. Therefore, we develop new methods with proper gaze guidance for 3D interaction in AR, and also an implicit online calibration method. We conduct two user studies to evaluate different interaction methods and the results show that our methods not only improve the effectiveness of occluded objects selection but also alleviate the arm fatigue problem significantly in the depth translation task. We also evaluate the proposed implicit online calibration method and find its accuracy comparable to standard 9 points explicit calibration, which makes a step towards practical use in the real world.
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Cited by top-tier papers7
- FocusFlow: 3D Gaze-Depth Interaction in Virtual Reality Leveraging Active Visual Depth ManipulationChenyang Zhang, Tiansu Chen, Eric Shaffer, Elahe SoltanaghaiCHI 2024 · 15 citations
- HandyNotes: using the hands to create semantic representations of contextually aware real-world objectsClément Quere, Aline Menin, Raphaël Julien, Hui-Yin Wu et al.IEEE VR 2024 · 2 citations
- PwP: Permutating with Probability for Efficient Group Selection in VRJian Wu, Weicheng Zhang, Handong Chen, Wei Lin et al.IEEE VR 2025
- Unsupervised Gaze Representation Learning from Multi-view Face ImagesYiwei Bao, Feng LuCVPR 2024
- Hybrid-Domain Adaptative Representation Learning for Gaze EstimationQida Tan, Hongyu Yang, Wenchao DuAAAI 2026
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