{"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/canonpose-self-supervised-monocular-3d-human","title":"CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild","arxiv_id":"2011.14679","date":"2020-11-30","proceeding":"CVPR 2021 1","authors":["Bastian Wandt","Marco Rudolph","Petrissa Zell","Helge Rhodin","Bodo Rosenhahn"],"abstract":"Human pose estimation from single images is a challenging problem in computer vision that requires large amounts of labeled training data to be solved accurately. Unfortunately, for many human activities (\\eg outdoor sports) such training data does not exist and is hard or even impossible to acquire with traditional motion capture systems. We propose a self-supervised approach that learns a single image 3D pose estimator from unlabeled multi-view data. To this end, we exploit multi-view consistency constraints to disentangle the observed 2D pose into the underlying 3D pose and camera rotation. In contrast to most existing methods, we do not require calibrated cameras and can therefore learn from moving cameras. Nevertheless, in the case of a static camera setup, we present an optional extension to include constant relative camera rotations over multiple views into our framework. Key to the success are new, unbiased reconstruction objectives that mix information across views and training samples. The proposed approach is evaluated on two benchmark datasets (Human3.6M and MPII-INF-3DHP) and on the in-the-wild SkiPose dataset.","url_abs":"https://arxiv.org/abs/2011.14679v1","url_pdf":"https://arxiv.org/pdf/2011.14679v1.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":"canonpose-self-supervised-monocular-3d-human","repo_url":"https://github.com/bastianwandt/CanonPose","is_official":1,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"3d-human-pose-estimation","task_name":"3D Human Pose Estimation"},{"task_slug":"monocular-3d-human-pose-estimation","task_name":"Monocular 3D Human Pose Estimation"},{"task_slug":"pose-estimation","task_name":"Pose Estimation"},{"task_slug":"weakly-supervised-3d-human-pose-estimation","task_name":"Weakly-supervised 3D Human Pose Estimation"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/3d-human-pose-estimation-on-human36m","task":"3D Human Pose Estimation","dataset":"Human3.6M","model":"CanonPose","rank_in_archive_order":88,"of":88,"metrics":{"Average MPJPE (mm)":"74.3","Multi-View or Monocular":"MultiView","Using 2D ground-truth joints":"No"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-mpi-inf-3dhp","task":"3D Human Pose Estimation","dataset":"MPI-INF-3DHP","model":"CanonPose","rank_in_archive_order":71,"of":108,"metrics":{"MPJPE":"104","PCK":"77"},"uses_additional_data":false},{"leaderboard":"/sota/3d-human-pose-estimation-on-skipose","task":"3D Human Pose Estimation","dataset":"SkiPose","model":"CanonPose","rank_in_archive_order":3,"of":3,"metrics":{"CPS":"108.7","MPJPE":"128.1","P-MPJPE":"89.6","PCK":"67.1"},"uses_additional_data":false},{"leaderboard":"/sota/weakly-supervised-3d-human-pose-estimation-on","task":"Weakly-supervised 3D Human Pose Estimation","dataset":"Human3.6M","model":"CanonPose","rank_in_archive_order":19,"of":33,"metrics":{"3D Annotations":"No","Average MPJPE (mm)":"74.3","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false}],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2011.14679","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}