Methods › Computer Vision › Pose Estimation Models › ZoomNet
ZoomNet
Introduced by Sheng Jin et al. in Whole-Body Human Pose Estimation in the Wild
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
ZoomNet is a 2D human whole-body pose estimation technique. It aims to localize dense landmarks on the entire human body including face, hands, body, and feet. ZoomNet follows the top-down paradigm. Given a human bounding box of each person, ZoomNet first localizes the easy-to-detect body keypoints and estimates the rough position of hands and face. Then it zooms in to focus on the hand/face areas and predicts keypoints using features with higher resolution for accurate localization. Unlike previous approaches which usually assemble multiple networks, ZoomNet has a single network that is end-to-end trainable. It unifies five network heads including the human body pose estimator, hand and face detectors, and hand and face pose estimators into a single network with shared low-level features.
Papers archive 2025-07-28
3 shown of 3, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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ZoomNAS: Searching for Whole-body Human Pose Estimation in the Wild 23 Aug 2022 · 1 repository · arXiv:2208.11547
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Zoom In and Out: A Mixed-scale Triplet Network for Camouflaged Object Detection 5 Mar 2022 · 1 repository · arXiv:2203.02688Syntology ran 12 of 13 samples · 1 unverified
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Whole-Body Human Pose Estimation in the Wild 23 Jul 2020 · 2 repositories · arXiv:2007.11858
Tasks archive 2025-07-28
11 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
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Categories archive 2025-07-28
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