{"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/pose-guided-structured-region-ensemble","title":"Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation","arxiv_id":"1708.03416","date":"2017-08-11","proceeding":null,"authors":["Xinghao Chen","Guijin Wang","Hengkai Guo","Cairong Zhang"],"abstract":"Hand pose estimation from a single depth image is an essential topic in\ncomputer vision and human computer interaction. Despite recent advancements in\nthis area promoted by convolutional neural network, accurate hand pose\nestimation is still a challenging problem. In this paper we propose a Pose\nguided structured Region Ensemble Network (Pose-REN) to boost the performance\nof hand pose estimation. The proposed method extracts regions from the feature\nmaps of convolutional neural network under the guide of an initially estimated\npose, generating more optimal and representative features for hand pose\nestimation. The extracted feature regions are then integrated hierarchically\naccording to the topology of hand joints by employing tree-structured fully\nconnections. A refined estimation of hand pose is directly regressed by the\nproposed network and the final hand pose is obtained by utilizing an iterative\ncascaded method. Comprehensive experiments on public hand pose datasets\ndemonstrate that our proposed method outperforms state-of-the-art algorithms.","url_abs":"http://arxiv.org/abs/1708.03416v2","url_pdf":"http://arxiv.org/pdf/1708.03416v2.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":"pose-guided-structured-region-ensemble","repo_url":"https://github.com/xinghaochen/Pose-REN","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"caffe2","reach":null}],"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":[{"leaderboard":"/sota/hand-pose-estimation-on-hands-2017","task":"Hand Pose Estimation","dataset":"HANDS 2017","model":"Pose-REN","rank_in_archive_order":8,"of":9,"metrics":{"Average 3D Error":"11.70"},"uses_additional_data":false},{"leaderboard":"/sota/hand-pose-estimation-on-icvl-hands","task":"Hand Pose Estimation","dataset":"ICVL Hands","model":"Pose-REN","rank_in_archive_order":10,"of":15,"metrics":{"Average 3D Error":"6.8"},"uses_additional_data":false},{"leaderboard":"/sota/hand-pose-estimation-on-msra-hands","task":"Hand Pose Estimation","dataset":"MSRA Hands","model":"Pose-REN","rank_in_archive_order":9,"of":11,"metrics":{"Average 3D Error":"8.6"},"uses_additional_data":false},{"leaderboard":"/sota/hand-pose-estimation-on-nyu-hands","task":"Hand Pose Estimation","dataset":"NYU Hands","model":"Pose-REN","rank_in_archive_order":14,"of":17,"metrics":{"Average 3D Error":"11.8"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":"https://app.syntology.ai/?focus=1708.03416","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}