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头眼K:在虚拟现实中学习的头眼协调和控制

Yifang Pan, Ludwig Sidenmark, Karan Singh

IEEE transactions on visualization and computer graphics

|July 15, 2025

概括

这项研究介绍了Head-EyeK,这是一个在虚拟现实中实现现实的人头眼协调的新型模型. 它通过整合生理和心理因素,准确地激活复杂的眼神行为.

科学领域:

人与计算机的交互
计算机图形计算机图形
心理学心理学心理学
机器人技术机器人技术机器人技术

背景情况:

人类的头眼协调是复杂的,受生理学,心理学和意图的影响.
心理学和图形学中现有的凝视模型缺乏对现实的头眼协调的概括性.

研究的目的:

开发和验证一个新的头眼协调的反向动态凝视模型 (Head-EyeK).
确定影响人类头眼协调的关键因素.
在虚拟现实中可信地激活复杂的凝视行为.

主要方法:

开发了一种实验性协议,在连续的观察任务中,在虚拟现实 (VR) 中收集头眼运动数据.
利用收集的数据来确定影响头眼协调的因素.
引入了Head-EyeK模型,将已识别的见解集成到一个反向运动框架中.

主要成果:

评估了Head-EyeK的算法参数对目光行为的影响.
使用基准真相数据,证明了对先前技术的定量优越性.
通过感知研究和复杂的目光行为可信的动画展示了定性改进.

结论:

在模拟人类头眼协调方面,Head-EyeK提供了显著的进步.
该模型可信地激活了复杂的目光行为,超过了现有的方法.
这项研究有助于在虚拟环境中实现更现实的人与人之间的交互.

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