@inproceedings{8e7f8d96a884474bb2315a8cf3906bb9,
title = "Gaze Pattern Genius: Gaze-Driven VR Interaction Using Unsupervised Domain Adaption",
abstract = "This research advocates shifting VR interaction to gaze-driven interaction, a more intuitive alternative to traditional controls like VR controllers or gestures. Our focus is on enhancing neural network recognition accuracy, especially with limited user-specific gaze data. We introduce a novel framework for capturing gaze gesture patterns and propose a template dataset concept to boost neural training. Our unsupervised domain adaptation model, blending template depth and sparse user data authenticity, consistently excels in recognizing gaze patterns across diverse users. Rigorous benchmarking against leading architectures consistently shows our method outperforming. Empirical user studies confirm: gaze-driven interactions not only elevate VR experiences but also redefine immersive VR control dynamics.",
keywords = "Domain adaption, Gaze gesture, Gaze-driven interaction, VR interaction",
author = "Kexin Wang and Yang Gao and Wenfeng Song and Yuecheng Li and Aimin Hao",
note = "Publisher Copyright: {\textcopyright} 2024 IEEE.; 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024 ; Conference date: 16-03-2024 Through 21-03-2024",
year = "2024",
doi = "10.1109/VRW62533.2024.00265",
language = "英语",
series = "Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "937--938",
booktitle = "Proceedings - 2024 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2024",
address = "美国",
}