GazeAgent：利用第一视角多模态行为与 VLM 引导实现 AI 眼镜隐式注视校准
GazeAgent: VLM-Guided Implicit Gaze Calibration from Egocentric Multimodal Behavior for AI Glasses
GazeAgent 是一个由视觉语言模型（VLM）引导的框架，可从惯性测量单元（IMU）、注视和佩戴者手部信号等多模态流中识别精确视觉注意（PVA）事件，验证其任务相关性，并把功能目标定位为伪校准目标，从而在不打断用户活动的情况下持续改善注视校准。
arXiv:2610.24364v1 Announce Type: new Abstract: AI glasses typically require explicit, user-specific eye-tracker calibration, and accuracy can drift when the device shifts during use. We present GazeAgent, a VLM-guided framework that continuously and adaptively improves gaze-calibration accuracy from naturally occurring behavior without asking users to interrupt their activities. GazeAgent identifies precise visual attention (PVA) events from multimodal streams of inertial measurement unit (IMU), gaze, and wearer-hand signals, validates their task relevance, and localizes functional targets as pseudo-calibration targets. In a 10-participant scripted study, the system detected all 820 predefined PVA opportunities and, under device disturbance, reduced median gaze error by 44.1% compared with explicit calibration. In a five-participant case study, autonomous calibration produced the lowest workload and interruption ratings, and all participants preferred it for routine use. By deriving task-critical targets from natural multimodal behavior, GazeAgent demonstrates the potential for continuous, adaptive, and low-burden gaze interaction on egocentric AI glasses.
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来源：arXiv · Human-Computer Interaction · arxiv.org