{"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/smap-single-shot-multi-person-absolute-3d","title":"SMAP: Single-Shot Multi-Person Absolute 3D Pose Estimation","arxiv_id":"2008.11469","date":"2020-08-26","proceeding":"ECCV 2020 8","authors":["Jianan Zhen","Qi Fang","Jiaming Sun","Wentao Liu","Wei Jiang","Hujun Bao","Xiaowei Zhou"],"abstract":"Recovering multi-person 3D poses with absolute scales from a single RGB image is a challenging problem due to the inherent depth and scale ambiguity from a single view. Addressing this ambiguity requires to aggregate various cues over the entire image, such as body sizes, scene layouts, and inter-person relationships. However, most previous methods adopt a top-down scheme that first performs 2D pose detection and then regresses the 3D pose and scale for each detected person individually, ignoring global contextual cues. In this paper, we propose a novel system that first regresses a set of 2.5D representations of body parts and then reconstructs the 3D absolute poses based on these 2.5D representations with a depth-aware part association algorithm. Such a single-shot bottom-up scheme allows the system to better learn and reason about the inter-person depth relationship, improving both 3D and 2D pose estimation. The experiments demonstrate that the proposed approach achieves the state-of-the-art performance on the CMU Panoptic and MuPoTS-3D datasets and is applicable to in-the-wild videos.","url_abs":"https://arxiv.org/abs/2008.11469v1","url_pdf":"https://arxiv.org/pdf/2008.11469v1.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":"smap-single-shot-multi-person-absolute-3d","repo_url":"https://github.com/zju3dv/smap","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"2d-pose-estimation","task_name":"2D Pose Estimation"},{"task_slug":"3d-depth-estimation","task_name":"3D Depth Estimation"},{"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-multi-person-pose-estimation-absolute","task_name":"3D Multi-Person Pose Estimation (absolute)"},{"task_slug":"3d-multi-person-pose-estimation-root-relative","task_name":"3D Multi-Person Pose Estimation (root-relative)"},{"task_slug":"3d-pose-estimation","task_name":"3D Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-multi-person-pose-estimation-on-cmu","task":"3D Multi-Person Pose Estimation","dataset":"Panoptic","model":"SMAP","rank_in_archive_order":18,"of":20,"metrics":{"Average MPJPE (mm)":"61.8"},"uses_additional_data":true},{"leaderboard":"/sota/3d-multi-person-pose-estimation-absolute-on","task":"3D Multi-Person Pose Estimation (absolute)","dataset":"MuPoTS-3D","model":"SMAP","rank_in_archive_order":11,"of":14,"metrics":{"3DPCK":"35.4"},"uses_additional_data":false},{"leaderboard":"/sota/3d-multi-person-pose-estimation-root-relative","task":"3D Multi-Person Pose Estimation (root-relative)","dataset":"MuPoTS-3D","model":"SMAP","rank_in_archive_order":16,"of":20,"metrics":{"3DPCK":"73.5"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2008.11469","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}