{"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/bighand22m-benchmark-hand-pose-dataset-and","title":"BigHand2.2M Benchmark: Hand Pose Dataset and State of the Art Analysis","arxiv_id":"1704.02612","date":"2017-04-09","proceeding":"CVPR 2017 7","authors":["Shanxin Yuan","Qi Ye","Bjorn Stenger","Siddhant Jain","Tae-Kyun Kim"],"abstract":"In this paper we introduce a large-scale hand pose dataset, collected using a\nnovel capture method. Existing datasets are either generated synthetically or\ncaptured using depth sensors: synthetic datasets exhibit a certain level of\nappearance difference from real depth images, and real datasets are limited in\nquantity and coverage, mainly due to the difficulty to annotate them. We\npropose a tracking system with six 6D magnetic sensors and inverse kinematics\nto automatically obtain 21-joints hand pose annotations of depth maps captured\nwith minimal restriction on the range of motion. The capture protocol aims to\nfully cover the natural hand pose space. As shown in embedding plots, the new\ndataset exhibits a significantly wider and denser range of hand poses compared\nto existing benchmarks. Current state-of-the-art methods are evaluated on the\ndataset, and we demonstrate significant improvements in cross-benchmark\nperformance. We also show significant improvements in egocentric hand pose\nestimation with a CNN trained on the new dataset.","url_abs":"http://arxiv.org/abs/1704.02612v2","url_pdf":"http://arxiv.org/pdf/1704.02612v2.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":[],"tasks":[{"task_slug":"art-analysis","task_name":"Art Analysis"},{"task_slug":"hand-pose-estimation","task_name":"Hand Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"bighand2-2m-benchmark","name":"BigHand2.2M Benchmark","full_name":""}],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1704.02612","atlas_url":"https://app.syntology.ai/?focus=1704.02612","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}