← 研踪 PaperTrailarXiv:2611.13640 · v2
EyeQ: Exploring Corneal Reflection for Mobile Context Sensing
首次提交 2026-11-10 14:34
版本更新 2026-11-12 10:56
雷达观测 2026-11-13 14:16
摘要
What if a single face-facing camera could sense your surroundings---through your eyes? We introduce EyeQ, which explores corneal reflection as a modality for context-aware mobile interaction. Traditional vision-based context sensing relies on world-facing cameras that demand explicit aiming or dual-camera setups; EyeQ instead shows that an off-the-shelf VLM can read scene context from corneal reflections captured with a smartphone camera. To recover usable detail from mobile corneal crops, EyeQ pairs on-device iris-guided capture with server-side AI enhancement, then leverages VLMs to extract scene types, names, and objects from warped, unfamiliar eye-image sequences. In an N=10 study, we characterize how capture conditions shape reflection fidelity, evaluate what VLMs detect, misclassify, and hallucinate, and demonstrate interaction opportunities, including detecting a steering wheel to distinguish drivers from passengers. This work reframes the eye in HCI---from a target for gaze estimation to a passive mirror of one's surroundings---and examines the privacy tensions it raises.
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v2 · 2026-11-12 10:56 · 最新观测版本
EyeQ: Exploring Corneal Reflection for Mobile Context Sensing
What if a single face-facing camera could sense your surroundings---through your eyes? We introduce EyeQ, which explores corneal reflection as a modality for context-aware mobile interaction. Traditional vision-based context sensing relies on world-facing cameras that demand explicit aiming or dual-camera setups; EyeQ instead shows that an off-the-shelf VLM can read scene context from corneal reflections captured with a smartphone camera. To recover usable detail from mobile corneal crops, EyeQ pairs on-device iris-guided capture with server-side AI enhancement, then leverages VLMs to extract scene types, names, and objects from warped, unfamiliar eye-image sequences. In an N=10 study, we characterize how capture conditions shape reflection fidelity, evaluate what VLMs detect, misclassify, and hallucinate, and demonstrate interaction opportunities, including detecting a steering wheel to distinguish drivers from passengers. This work reframes the eye in HCI---from a target for gaze estimation to a passive mirror of one's surroundings---and examines the privacy tensions it raises.
查看此版本解读 →查看此版本原文 ↗v1 · 2026-11-10 14:34
EyeQ: Exploring Corneal Reflection for Mobile Context Sensing
What if a single face-facing camera could sense your surroundings---through your eyes? We introduce EyeQ, which explores corneal reflection as a modality for context-aware mobile interaction. Traditional vision-based context sensing relies on world-facing cameras that demand explicit aiming or dual-camera setups; EyeQ instead shows that an off-the-shelf VLM can read scene context from corneal reflections captured with a smartphone camera. To recover usable detail from mobile corneal crops, EyeQ pairs on-device iris-guided capture with server-side AI enhancement, then leverages VLMs to extract scene types, names, and objects from warped, unfamiliar eye-image sequences. In an N=10 study, we characterize how capture conditions shape reflection fidelity, evaluate what VLMs detect, misclassify, and hallucinate, and demonstrate interaction opportunities, including detecting a steering wheel to distinguish drivers from passengers. This work reframes the eye in HCI---from a target for gaze estimation to a passive mirror of one's surroundings---and examines the privacy tensions it raises.
查看此版本解读 →查看此版本原文 ↗