{"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/deep-pictorial-gaze-estimation","title":"Deep Pictorial Gaze Estimation","arxiv_id":"1807.10002","date":"2018-07-26","proceeding":"ECCV 2018 9","authors":["Seonwook Park","Adrian Spurr","Otmar Hilliges"],"abstract":"Estimating human gaze from natural eye images only is a challenging task.\nGaze direction can be defined by the pupil- and the eyeball center where the\nlatter is unobservable in 2D images. Hence, achieving highly accurate gaze\nestimates is an ill-posed problem. In this paper, we introduce a novel deep\nneural network architecture specifically designed for the task of gaze\nestimation from single eye input. Instead of directly regressing two angles for\nthe pitch and yaw of the eyeball, we regress to an intermediate pictorial\nrepresentation which in turn simplifies the task of 3D gaze direction\nestimation. Our quantitative and qualitative results show that our approach\nachieves higher accuracies than the state-of-the-art and is robust to variation\nin gaze, head pose and image quality.","url_abs":"http://arxiv.org/abs/1807.10002v1","url_pdf":"http://arxiv.org/pdf/1807.10002v1.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":"deep-pictorial-gaze-estimation","repo_url":"https://github.com/xiamenwcy/pictorial_net","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"gaze-estimation","task_name":"Gaze Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1807.10002","atlas_url":"https://app.syntology.ai/?focus=1807.10002","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10002"}},"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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