{"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/towards-highly-accurate-and-stable-face","title":"Towards Highly Accurate and Stable Face Alignment for High-Resolution Videos","arxiv_id":"1811.00342","date":"2018-11-01","proceeding":null,"authors":["Ying Tai","Yicong Liang","Xiaoming Liu","Lei Duan","Jilin Li","Chengjie Wang","Feiyue Huang","Yu Chen"],"abstract":"In recent years, heatmap regression based models have shown their\neffectiveness in face alignment and pose estimation. However, Conventional\nHeatmap Regression (CHR) is not accurate nor stable when dealing with\nhigh-resolution facial videos, since it finds the maximum activated location in\nheatmaps which are generated from rounding coordinates, and thus leads to\nquantization errors when scaling back to the original high-resolution space. In\nthis paper, we propose a Fractional Heatmap Regression (FHR) for\nhigh-resolution video-based face alignment. The proposed FHR can accurately\nestimate the fractional part according to the 2D Gaussian function by sampling\nthree points in heatmaps. To further stabilize the landmarks among continuous\nvideo frames while maintaining the precise at the same time, we propose a novel\nstabilization loss that contains two terms to address time delay and non-smooth\nissues, respectively. Experiments on 300W, 300-VW and Talking Face datasets\nclearly demonstrate that the proposed method is more accurate and stable than\nthe state-of-the-art models.","url_abs":"http://arxiv.org/abs/1811.00342v2","url_pdf":"http://arxiv.org/pdf/1811.00342v2.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":"towards-highly-accurate-and-stable-face","repo_url":"https://github.com/tyshiwo/FHR_alignment","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"ok"}}],"tasks":[{"task_slug":"face-alignment","task_name":"Face Alignment"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"quantization","task_name":"Quantization"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"},{"task_slug":"regression-1","task_name":"regression"}],"methods":[{"method_slug":"heatmap","method_name":"Heatmap"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.00342","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}