{"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/3d-pictorial-structures-for-multiple-human","title":"3D Pictorial Structures for Multiple Human Pose Estimation","arxiv_id":null,"date":"2014-06-01","proceeding":"CVPR 2014 6","authors":["Vasileios Belagiannis","Sikandar Amin","Mykhaylo Andriluka","Bernt Schiele","Nassir Navab","Slobodan Ilic"],"abstract":"In this work, we address the problem of 3D pose estimation of multiple humans from multiple views. This is a more challenging problem than single human 3D pose estimation due to the much larger state space, partial occlusions as well as across view ambiguities when not knowing the identity of the humans in advance. To address these problems, we first create a reduced state space by triangulation of corresponding body joints obtained from part detectors  in pairs of camera views. In order to resolve the ambiguities of wrong and mixed body parts of multiple humans after triangulation and also those coming from false positive body part detections, we introduce a novel 3D pictorial structures (3DPS) model. Our model infers 3D human body configurations from our reduced state space. The 3DPS model is generic and applicable to both single and multiple human pose estimation.  In order to compare to the state-of-the art, we first evaluate our method on single human 3D pose estimation on HumanEva-I [22] and  KTH Multiview Football Dataset II [8] datasets. Then, we introduce and evaluate our method on two datasets for multiple human 3D pose estimation.","url_abs":"http://openaccess.thecvf.com/content_cvpr_2014/html/Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper.html","url_pdf":"http://openaccess.thecvf.com/content_cvpr_2014/papers/Belagiannis_3D_Pictorial_Structures_2014_CVPR_paper.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":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"3d-multi-person-pose-estimation","task_name":"3D Multi-Person Pose Estimation"},{"task_slug":"3d-pose-estimation","task_name":"3D Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[{"slug":"campus-shelf","name":"Campus & Shelf","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-multi-person-pose-estimation-on-shelf","task":"3D Multi-Person Pose Estimation","dataset":"Shelf","model":"Belagiannis model v.1","rank_in_archive_order":27,"of":27,"metrics":{"PCP3D":"71.4"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}