发表机构
KAIST; Google; University of California, Los Angeles（韩国科学技术院; 谷歌; 加州大学洛杉矶分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
GazeTune结合眼动与触摸的级联多模态技术，在2D拖动任务中相比仅眼动、眼动-捏合方法，误差显著更低且执行时间相当，实现了时间与精度的平衡。
AI 中文摘要
眼动已成为空间计算的重要输入方式，但其定位粗糙、存在眼跳特性，限制了精度，也使拖动等连续交互变得复杂，尤其在用户移动时；眼动+捏合已成为XR中的标准方式，因其便捷性，但空中手势仍不够精确、易致疲劳且社交接受度低。这些局限性凸显出需要一种既保留眼动速度又能实现稳定精细控制的方法。本文提出GazeTune，一种结合眼动与触摸的级联多模态交互技术，用于优化基于眼动的选择与操作；触摸作为眼动指向过程中的优化通道，可实现精确的光标与目标控制。本研究探讨眼动-触摸组合如何增强拖动操作并缓解运动诱导的不稳定性。在一项含20名被试的研究中，我们在2D拖动任务中对比了GazeTune与仅眼动、眼动-捏合方法的性能，结果显示GazeTune的误差显著更低，执行时间相当，验证了其有效性以及在时间与精度间的平衡权衡。
英文摘要
Eye gaze has become an essential input for spatial computing, but its coarse targeting and saccadic nature limit precision and complicate continuous interactions such as dragging, especially under user motion. Gaze+pinch has also become standard in XR for its convenience, yet mid-air gestures remain imprecise, fatiguing, and socially unacceptable. These limitations underscore the need for an approach that preserves the speed of gaze while enabling stable, fine control. We present GazeTune, a cascaded multimodal interaction technique combining gaze and touch to refine gaze-based selection and manipulation. Touch serves as a refinement channel within gaze pointing, allowing precise cursor and target control. Our work investigates how gaze-and-touch enhances dragging and mitigates Motion-Induced instability. In a study (N=20), we compared GazeTune against gaze-only and gaze-pinch methods in 2D dragging. Results show that GazeTune achieves significantly lower error with comparable execution time, validating its effectiveness and balanced trade-off between time and accuracy.
Comments13 pages, 8 Figures, Accepted to UIST'26
