{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/eye-tracking-for-everyone","title":"Eye Tracking for Everyone","arxiv_id":"1606.05814","date":"2016-06-18","proceeding":"CVPR 2016 6","authors":["Kyle Krafka","Aditya Khosla","Petr Kellnhofer","Harini Kannan","Suchendra Bhandarkar","Wojciech Matusik","Antonio Torralba"],"abstract":"From scientific research to commercial applications, eye tracking is an\nimportant tool across many domains. Despite its range of applications, eye\ntracking has yet to become a pervasive technology. We believe that we can put\nthe power of eye tracking in everyone's palm by building eye tracking software\nthat works on commodity hardware such as mobile phones and tablets, without the\nneed for additional sensors or devices. We tackle this problem by introducing\nGazeCapture, the first large-scale dataset for eye tracking, containing data\nfrom over 1450 people consisting of almost 2.5M frames. Using GazeCapture, we\ntrain iTracker, a convolutional neural network for eye tracking, which achieves\na significant reduction in error over previous approaches while running in real\ntime (10-15fps) on a modern mobile device. Our model achieves a prediction\nerror of 1.71cm and 2.53cm without calibration on mobile phones and tablets\nrespectively. With calibration, this is reduced to 1.34cm and 2.12cm. Further,\nwe demonstrate that the features learned by iTracker generalize well to other\ndatasets, achieving state-of-the-art results. The code, data, and models are\navailable at http://gazecapture.csail.mit.edu.","url_abs":"http://arxiv.org/abs/1606.05814v1","url_pdf":"http://arxiv.org/pdf/1606.05814v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"eye-tracking-for-everyone","repo_url":"https://github.com/hugochan/Eye-Tracker","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"BSD-3-Clause"}},{"paper_slug":"eye-tracking-for-everyone","repo_url":"https://github.com/CSAILVision/GazeCapture","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"NOASSERTION"}}],"tasks":[{"task_slug":"gaze-estimation","task_name":"Gaze Estimation"}],"methods":[],"datasets_introduced":[{"slug":"gazecapture","name":"GazeCapture","full_name":"Eye Tracking for Everyone"}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1606.05814","atlas_url":"https://app.syntology.ai/?focus=1606.05814","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1606.05814"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-25T09:33:49+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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