{"paper_id":"20b2d353-ba09-4618-becd-cc90aaeee1f6","body_text":"Enhanced Human Pose Estimation via Self-Distilled and Token- Pruned Transformer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Enhanced Human Pose Estimation via Self-Distilled and Token- Pruned Transformer Jundu Zhang, Zhengjie Deng, Xiyan Li, Sijian Yan, Zhirui Li, Chang Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8088041/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Human pose estimation (HPE) is a fundamental challenge in computer vision, aiming to detect anatomical keypoints in images. Traditional methods rely on CNN models, but recent advancements in Vision Transformer (ViT) models have shown superior performance. However, ViTs often require substantial computational resources. This paper introduces SPTPose, a method that employs self-distillation and token pruning to reduce computational costs while maintaining high performance. Our SPTPose-B achieves a mAP of 74.8% on the MSCOCO validation set with only 13.2 million parameters and 4.7 GFLOPs. The source code is available at https://github.com/duduxx123/SPTPose . Biological sciences/Computational biology and bioinformatics Physical sciences/Engineering Physical sciences/Mathematics and computing Human Pose Estimation Self-distillation Deep Learning Heatmap Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 11 Dec, 2025 Reviews received at journal 02 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers agreed at journal 01 Dec, 2025 Reviewers invited by journal 01 Dec, 2025 Editor assigned by journal 01 Dec, 2025 Editor invited by journal 21 Nov, 2025 Submission checks completed at journal 16 Nov, 2025 First submitted to journal 16 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Reports\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"stoa\",\"reportingPortfolio\":\"Scientific Reports\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Human Pose Estimation, Self-distillation, Deep Learning, Heatmap\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8088041/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8088041/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eHuman pose estimation (HPE) is a fundamental challenge in computer vision, aiming to detect anatomical keypoints in images. Traditional methods rely on CNN models, but recent advancements in Vision Transformer (ViT) models have shown superior performance. However, ViTs often require substantial computational resources. This paper introduces SPTPose, a method that employs self-distillation and token pruning to reduce computational costs while maintaining high performance. Our SPTPose-B achieves a mAP of 74.8% on the MSCOCO validation set with only 13.2\\u0026nbsp;million parameters and 4.7 GFLOPs. 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