{"ID":2877444,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2508.19544","arxiv_id":"2508.19544","title":"WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization","abstract":"With advancements in AI, new gaze estimation methods are exceeding state-of-the-art (SOTA) benchmarks, but their real-world application reveals a gap with commercial eye-tracking solutions. Factors like model size, inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods lack sufficient accuracy, in particular due to head movement. To tackle these issues, we introduce We bEyeTrack, a framework that integrates lightweight SOTA gaze estimation models directly in the browser. It incorporates model-based head pose estimation and on-device few-shot learning with as few as nine calibration samples (k \u003c 9). WebEyeTrack adapts to new users, achieving SOTA performance with an error margin of 2.32 cm on GazeCapture and real-time inference speeds of 2.4 milliseconds on an iPhone 14. Our open-source code is available at https://github.com/RedForestAi/WebEyeTrack.","short_abstract":"With advancements in AI, new gaze estimation methods are exceeding state-of-the-art (SOTA) benchmarks, but their real-world application reveals a gap with commercial eye-tracking solutions. Factors like model size, inference time, and privacy often go unaddressed. Meanwhile, webcam-based eye-tracking methods lack suffi...","url_abs":"https://arxiv.org/abs/2508.19544","url_pdf":"https://arxiv.org/pdf/2508.19544v1","authors":"[\"Eduardo Davalos\",\"Yike Zhang\",\"Namrata Srivastava\",\"Yashvitha Thatigotla\",\"Jorge A. Salas\",\"Sara McFadden\",\"Sun-Joo Cho\",\"Amanda Goodwin\",\"Ashwin TS\",\"Gautam Biswas\"]","published":"2025-08-27T03:38:58Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[]","has_code":false,"code_links":[{"ID":610379,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2877444,"paper_url":"https://arxiv.org/abs/2508.19544","paper_title":"WEBEYETRACK: Scalable Eye-Tracking for the Browser via On-Device Few-Shot Personalization","repo_url":"https://github.com/RedForestAi/WebEyeTrack","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}