{"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/end-to-end-estimation-of-multi-person-3d","title":"VoxelPose: Towards Multi-Camera 3D Human Pose Estimation in Wild Environment","arxiv_id":"2004.06239","date":"2020-04-13","proceeding":"ECCV 2020 8","authors":["Hanyue Tu","Chunyu Wang","Wen-Jun Zeng"],"abstract":"We present an approach to estimate 3D poses of multiple people from multiple camera views. In contrast to the previous efforts which require to establish cross-view correspondence based on noisy and incomplete 2D pose estimations, we present an end-to-end solution which directly operates in the $3$D space, therefore avoids making incorrect decisions in the 2D space. To achieve this goal, the features in all camera views are warped and aggregated in a common 3D space, and fed into Cuboid Proposal Network (CPN) to coarsely localize all people. Then we propose Pose Regression Network (PRN) to estimate a detailed 3D pose for each proposal. The approach is robust to occlusion which occurs frequently in practice. Without bells and whistles, it outperforms the state-of-the-arts on the public datasets. Code will be released at https://github.com/microsoft/multiperson-pose-estimation-pytorch.","url_abs":"https://arxiv.org/abs/2004.06239v4","url_pdf":"https://arxiv.org/pdf/2004.06239v4.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":"end-to-end-estimation-of-multi-person-3d","repo_url":"https://github.com/microsoft/voxelpose-pytorch","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"pytorch","reach":null},{"paper_slug":"end-to-end-estimation-of-multi-person-3d","repo_url":"https://github.com/open-mmlab/mmpose","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"Apache-2.0"}}],"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":"pose-estimation","task_name":"Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-multi-person-pose-estimation-on-campus","task":"3D Multi-Person Pose Estimation","dataset":"Campus","model":"VoxelPose","rank_in_archive_order":7,"of":16,"metrics":{"PCP3D":"96.7"},"uses_additional_data":false},{"leaderboard":"/sota/3d-multi-person-pose-estimation-on-cmu","task":"3D Multi-Person Pose Estimation","dataset":"Panoptic","model":"VoxelPose","rank_in_archive_order":5,"of":20,"metrics":{"Average MPJPE (mm)":"17.68"},"uses_additional_data":true},{"leaderboard":"/sota/3d-multi-person-pose-estimation-on-shelf","task":"3D Multi-Person Pose Estimation","dataset":"Shelf","model":"VoxelPose","rank_in_archive_order":17,"of":27,"metrics":{"PCP3D":"97"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}