{"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/multiposenet-fast-multi-person-pose","title":"MultiPoseNet: Fast Multi-Person Pose Estimation using Pose Residual Network","arxiv_id":"1807.04067","date":"2018-07-11","proceeding":"ECCV 2018 9","authors":["Muhammed Kocabas","Salih Karagoz","Emre Akbas"],"abstract":"In this paper, we present MultiPoseNet, a novel bottom-up multi-person pose\nestimation architecture that combines a multi-task model with a novel\nassignment method. MultiPoseNet can jointly handle person detection, keypoint\ndetection, person segmentation and pose estimation problems. The novel\nassignment method is implemented by the Pose Residual Network (PRN) which\nreceives keypoint and person detections, and produces accurate poses by\nassigning keypoints to person instances. On the COCO keypoints dataset, our\npose estimation method outperforms all previous bottom-up methods both in\naccuracy (+4-point mAP over previous best result) and speed; it also performs\non par with the best top-down methods while being at least 4x faster. Our\nmethod is the fastest real time system with 23 frames/sec. Source code is\navailable at: https://github.com/mkocabas/pose-residual-network","url_abs":"http://arxiv.org/abs/1807.04067v1","url_pdf":"http://arxiv.org/pdf/1807.04067v1.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":"multiposenet-fast-multi-person-pose","repo_url":"https://github.com/SukhyunCho/NTU_motion_sim_annotations","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"multiposenet-fast-multi-person-pose","repo_url":"https://github.com/danielperezr88/multiposenet-aries","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"multiposenet-fast-multi-person-pose","repo_url":"https://github.com/eric-erki/pose-residual-network-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"multiposenet-fast-multi-person-pose","repo_url":"https://github.com/salihkaragoz/pose-residual-network-pytorch","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"human-detection","task_name":"Human Detection"},{"task_slug":"keypoint-detection","task_name":"Keypoint Detection"},{"task_slug":"multi-person-pose-estimation","task_name":"Multi-Person Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/keypoint-detection-on-coco","task":"Keypoint Detection","dataset":"COCO (Common Objects in Context)","model":"Pose Residual Network","rank_in_archive_order":23,"of":24,"metrics":{"Validation AP":"69.6"},"uses_additional_data":false},{"leaderboard":"/sota/multi-person-pose-estimation-on-coco","task":"Multi-Person Pose Estimation","dataset":"COCO (Common Objects in Context)","model":"Pose Residual Network","rank_in_archive_order":8,"of":15,"metrics":{"AP":"0.697"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1807.04067","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}