{"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/adversarial-posenet-a-structure-aware","title":"Adversarial PoseNet: A Structure-aware Convolutional Network for Human Pose Estimation","arxiv_id":"1705.00389","date":"2017-04-30","proceeding":"ICCV 2017 10","authors":["Yu Chen","Chunhua Shen","Xiu-Shen Wei","Lingqiao Liu","Jian Yang"],"abstract":"For human pose estimation in monocular images, joint occlusions and\noverlapping upon human bodies often result in deviated pose predictions. Under\nthese circumstances, biologically implausible pose predictions may be produced.\nIn contrast, human vision is able to predict poses by exploiting geometric\nconstraints of joint inter-connectivity. To address the problem by\nincorporating priors about the structure of human bodies, we propose a novel\nstructure-aware convolutional network to implicitly take such priors into\naccount during training of the deep network. Explicit learning of such\nconstraints is typically challenging. Instead, we design discriminators to\ndistinguish the real poses from the fake ones (such as biologically implausible\nones). If the pose generator (G) generates results that the discriminator fails\nto distinguish from real ones, the network successfully learns the priors.","url_abs":"http://arxiv.org/abs/1705.00389v2","url_pdf":"http://arxiv.org/pdf/1705.00389v2.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":"adversarial-posenet-a-structure-aware","repo_url":"https://github.com/rohitrango/Adversarial-Pose-Estimation","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}},{"paper_slug":"adversarial-posenet-a-structure-aware","repo_url":"https://github.com/Mind23-2/MindCode-5/tree/main/PoseNet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"mindspore","reach":null}],"tasks":[{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/pose-estimation-on-mpii-human-pose","task":"Pose Estimation","dataset":"MPII Human Pose","model":"Chen et al. ICCV'17","rank_in_archive_order":15,"of":46,"metrics":{"PCKh-0.5":"91.9"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1705.00389","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}