GazeDock: Gaze-Only Menu Selection in Virtual Reality using Auto-Triggering Peripheral Menu
Xin Yi, Yiqin Lu, Ziyin Cai, Zihan Wu, Yuntao Wang, Yuanchun Shi
摘要
Gaze-only input techniques in VR face the challenge of avoiding false triggering due to continuous eye tracking while maintaining interaction performance. In this paper, we proposed GazeDock, a technique for enabling fast and robust gaze-based menu selection in VR. GazeDock features a view-fixed peripheral menu layout that automatically triggers appearing and selection when the user’s gaze approaches and leaves the menu zone, thus facilitating interaction speed and minimizing the false triggering rate. We built a dataset of 12 participants’ natural gaze movements in typical VR applications. By analyzing their gaze movement patterns, we designed the menu UI personalization and optimized selection detection algorithm of GazeDock. We also examined users’ gaze selection precision for targets on the peripheral menu and found that 4–8 menu items yield the highest throughput when considering both speed and accuracy. Finally, we validated the usability of GazeDock in a VR navigation game that contains both scene exploration and menu selection. Results showed that GazeDock achieved an average selection time of 471ms and a false triggering rate of 3.6%. And it received higher user preference ratings compared with dwell-based and pursuit-based techniques.
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- Unsupervised Gaze Representation Learning from Multi-view Face ImagesYiwei Bao, Feng LuCVPR 2024
- Semi-Supervised Gaze Estimation via Disentangled Subspace Contrastive LearningQida Tan, Hongyu Yang, Wenchao DuICML 2026
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