{"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/efficiently-creating-3d-training-data-for","title":"Efficiently Creating 3D Training Data for Fine Hand Pose Estimation","arxiv_id":"1605.03389","date":"2016-05-11","proceeding":"CVPR 2016 6","authors":["Markus Oberweger","Gernot Riegler","Paul Wohlhart","Vincent Lepetit"],"abstract":"While many recent hand pose estimation methods critically rely on a training\nset of labelled frames, the creation of such a dataset is a challenging task\nthat has been overlooked so far. As a result, existing datasets are limited to\na few sequences and individuals, with limited accuracy, and this prevents these\nmethods from delivering their full potential. We propose a semi-automated\nmethod for efficiently and accurately labeling each frame of a hand depth video\nwith the corresponding 3D locations of the joints: The user is asked to provide\nonly an estimate of the 2D reprojections of the visible joints in some\nreference frames, which are automatically selected to minimize the labeling\nwork by efficiently optimizing a sub-modular loss function. We then exploit\nspatial, temporal, and appearance constraints to retrieve the full 3D poses of\nthe hand over the complete sequence. We show that this data can be used to\ntrain a recent state-of-the-art hand pose estimation method, leading to\nincreased accuracy. The code and dataset can be found on our website\nhttps://cvarlab.icg.tugraz.at/projects/hand_detection/","url_abs":"http://arxiv.org/abs/1605.03389v2","url_pdf":"http://arxiv.org/pdf/1605.03389v2.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":"efficiently-creating-3d-training-data-for","repo_url":"https://github.com/moberweger/semi-auto-anno","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"hand-pose-estimation","task_name":"Hand Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1605.03389","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}