{"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/fast-human-pose-estimation","title":"Fast Human Pose Estimation","arxiv_id":"1811.05419","date":"2018-11-13","proceeding":"CVPR 2019 6","authors":["Feng Zhang","Xiatian Zhu","Mao Ye"],"abstract":"Existing human pose estimation approaches often only consider how to improve\nthe model generalisation performance, but putting aside the significant\nefficiency problem. This leads to the development of heavy models with poor\nscalability and cost-effectiveness in practical use. In this work, we\ninvestigate the under-studied but practically critical pose model efficiency\nproblem. To this end, we present a new Fast Pose Distillation (FPD) model\nlearning strategy. Specifically, the FPD trains a lightweight pose neural\nnetwork architecture capable of executing rapidly with low computational cost.\nIt is achieved by effectively transferring the pose structure knowledge of a\nstrong teacher network. Extensive evaluations demonstrate the advantages of our\nFPD method over a broad range of state-of-the-art pose estimation approaches in\nterms of model cost-effectiveness on two standard benchmark datasets, MPII\nHuman Pose and Leeds Sports Pose.","url_abs":"http://arxiv.org/abs/1811.05419v2","url_pdf":"http://arxiv.org/pdf/1811.05419v2.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":"fast-human-pose-estimation","repo_url":"https://github.com/ilovepose/fast-human-pose-estimation.pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pose-estimation-on-leeds-sports-poses","task":"Pose Estimation","dataset":"Leeds Sports Poses","model":"FPD","rank_in_archive_order":9,"of":18,"metrics":{"PCK":"90.8%"},"uses_additional_data":false},{"leaderboard":"/sota/pose-estimation-on-mpii-human-pose","task":"Pose Estimation","dataset":"MPII Human Pose","model":"FPD","rank_in_archive_order":22,"of":46,"metrics":{"PCKh-0.5":"91.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1811.05419","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}