{"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/appearance-based-gaze-estimation-in-the-wild","title":"Appearance-Based Gaze Estimation in the Wild","arxiv_id":"1504.02863","date":"2015-04-11","proceeding":"CVPR 2015 6","authors":["Xucong Zhang","Yusuke Sugano","Mario Fritz","Andreas Bulling"],"abstract":"Appearance-based gaze estimation is believed to work well in real-world\nsettings, but existing datasets have been collected under controlled laboratory\nconditions and methods have been not evaluated across multiple datasets. In\nthis work we study appearance-based gaze estimation in the wild. We present the\nMPIIGaze dataset that contains 213,659 images we collected from 15 participants\nduring natural everyday laptop use over more than three months. Our dataset is\nsignificantly more variable than existing ones with respect to appearance and\nillumination. We also present a method for in-the-wild appearance-based gaze\nestimation using multimodal convolutional neural networks that significantly\noutperforms state-of-the art methods in the most challenging cross-dataset\nevaluation. We present an extensive evaluation of several state-of-the-art\nimage-based gaze estimation algorithms on three current datasets, including our\nown. This evaluation provides clear insights and allows us to identify key\nresearch challenges of gaze estimation in the wild.","url_abs":"http://arxiv.org/abs/1504.02863v1","url_pdf":"http://arxiv.org/pdf/1504.02863v1.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":"appearance-based-gaze-estimation-in-the-wild","repo_url":"https://github.com/dongzelian/multi-view-gaze","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"appearance-based-gaze-estimation-in-the-wild","repo_url":"https://github.com/dreamer-1996/eye-tracking-with-resnet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}},{"paper_slug":"appearance-based-gaze-estimation-in-the-wild","repo_url":"https://github.com/hysts/pytorch_mpiigaze","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}},{"paper_slug":"appearance-based-gaze-estimation-in-the-wild","repo_url":"https://github.com/kenkyusha/eyeGazeToScreen","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"appearance-based-gaze-estimation-in-the-wild","repo_url":"https://github.com/kroniidvul/mpiigaze_project","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"appearance-based-gaze-estimation-in-the-wild","repo_url":"https://github.com/songNew/3_Gaze_MPII","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"gaze-estimation","task_name":"Gaze Estimation"}],"methods":[],"datasets_introduced":[{"slug":"mpiigaze","name":"MPIIGaze","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1504.02863","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1504.02863"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+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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