{"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/personlab-person-pose-estimation-and-instance","title":"PersonLab: Person Pose Estimation and Instance Segmentation with a Bottom-Up, Part-Based, Geometric Embedding Model","arxiv_id":"1803.08225","date":"2018-03-22","proceeding":"ECCV 2018 9","authors":["George Papandreou","Tyler Zhu","Liang-Chieh Chen","Spyros Gidaris","Jonathan Tompson","Kevin Murphy"],"abstract":"We present a box-free bottom-up approach for the tasks of pose estimation and\ninstance segmentation of people in multi-person images using an efficient\nsingle-shot model. The proposed PersonLab model tackles both semantic-level\nreasoning and object-part associations using part-based modeling. Our model\nemploys a convolutional network which learns to detect individual keypoints and\npredict their relative displacements, allowing us to group keypoints into\nperson pose instances. Further, we propose a part-induced geometric embedding\ndescriptor which allows us to associate semantic person pixels with their\ncorresponding person instance, delivering instance-level person segmentations.\nOur system is based on a fully-convolutional architecture and allows for\nefficient inference, with runtime essentially independent of the number of\npeople present in the scene. Trained on COCO data alone, our system achieves\nCOCO test-dev keypoint average precision of 0.665 using single-scale inference\nand 0.687 using multi-scale inference, significantly outperforming all previous\nbottom-up pose estimation systems. We are also the first bottom-up method to\nreport competitive results for the person class in the COCO instance\nsegmentation task, achieving a person category average precision of 0.417.","url_abs":"http://arxiv.org/abs/1803.08225v1","url_pdf":"http://arxiv.org/pdf/1803.08225v1.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":"personlab-person-pose-estimation-and-instance","repo_url":"https://github.com/SAtacker/PosenetTflite","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"LGPL-3.0"}},{"paper_slug":"personlab-person-pose-estimation-and-instance","repo_url":"https://github.com/google-coral/project-posenet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok","spdx":"Apache-2.0"}},{"paper_slug":"personlab-person-pose-estimation-and-instance","repo_url":"https://github.com/jp-sm/jp-sm.github.io","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":{"status":"ok"}}],"tasks":[{"task_slug":"instance-segmentation","task_name":"Instance Segmentation"},{"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"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/keypoint-detection-on-coco","task":"Keypoint Detection","dataset":"COCO (Common Objects in Context)","model":"PersonLab","rank_in_archive_order":14,"of":24,"metrics":{"Test AP":"66.5"},"uses_additional_data":false},{"leaderboard":"/sota/multi-person-pose-estimation-on-coco-test-dev","task":"Multi-Person Pose Estimation","dataset":"COCO test-dev","model":"PersonLab","rank_in_archive_order":8,"of":15,"metrics":{"AP":"68.7","AP50":"89.0","AP75":"75.4","APL":"75.5","APM":"64.1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1803.08225","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}