{"url":"/sota/weakly-supervised-3d-human-pose-estimation-on","task":{"name":"Weakly-supervised 3D Human Pose Estimation","url":"/task/weakly-supervised-3d-human-pose-estimation","note":null},"dataset":{"name":"Human3.6M","url":"/dataset/human3-6m"},"category":"Computer Vision","categories":["Computer Vision"],"category_note":null,"description":"This task targets at 3D Human Pose Estimation with fewer 3D annotation.","description_from":"task","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","rank":"the archive's row order at snapshot; not re-ranked","rows_end_at":"2025-07-28","rows_withheld_as_spam":0,"metric_values":"the archive's strings, untouched"},"metrics":["Average MPJPE (mm)","Number of Views","Number of Frames Per View","3D Annotations","PA-MPJPE"],"metric_direction":{"note":"inferred from the metric name only (the archive records no direction); null = not inferred, chart draws points only","by_metric":{"Average MPJPE (mm)":null,"Number of Views":null,"Number of Frames Per View":null,"3D Annotations":null,"PA-MPJPE":null}},"counts":{"rows":33,"rows_with_code":23,"rows_with_paper_page":33,"rows_dated":33,"rows_using_additional_data":0},"rows":[{"rank_in_archive_order":1,"model":"AdaptPose","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"42.5","Number of Frames Per View":"27","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2021-12-22","paper":"/paper/adaptpose-cross-dataset-adaptation-for-3d","paper_url":"https://arxiv.org/abs/2112.11593v2","paper_title":"AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion Generation","code":"https://github.com/mgholamikn/AdaptPose","n_code_links":1,"syntology":{"n_ran":1,"n_unverified":2,"n_samples":3,"n_pointer_only_licence":0}},{"rank_in_archive_order":2,"model":"DAF-DG-27frames","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"44.1","Number of Frames Per View":"27","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2024-03-17","paper":"/paper/a-dual-augmentor-framework-for-domain","paper_url":"https://arxiv.org/abs/2403.11310v2","paper_title":"A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation","code":"https://github.com/davidpengucf/daf-dg","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":3,"n_samples":5,"n_pointer_only_licence":5}},{"rank_in_archive_order":3,"model":"HDVR","metrics":{"Average MPJPE (mm)":"47.3"},"uses_additional_data":false,"paper_date":"2021-09-19","paper":"/paper/unsupervised-3d-pose-estimation-for","paper_url":"https://arxiv.org/abs/2109.09166v1","paper_title":"Unsupervised 3D Pose Estimation for Hierarchical Dance Video Recognition","code":"https://github.com/garfield-kh/posetriplet","n_code_links":1,"syntology":null},{"rank_in_archive_order":4,"model":"DAF-DG","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"50.3","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2024-03-17","paper":"/paper/a-dual-augmentor-framework-for-domain","paper_url":"https://arxiv.org/abs/2403.11310v2","paper_title":"A Dual-Augmentor Framework for Domain Generalization in 3D Human Pose Estimation","code":"https://github.com/davidpengucf/daf-dg","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":3,"n_samples":5,"n_pointer_only_licence":5}},{"rank_in_archive_order":5,"model":"PGNIS","metrics":{"Average MPJPE (mm)":"50.8"},"uses_additional_data":false,"paper_date":"2020-04-09","paper":"/paper/self-supervised-3d-human-pose-estimation-via","paper_url":"https://arxiv.org/abs/2004.04400v1","paper_title":"Self-Supervised 3D Human Pose Estimation via Part Guided Novel Image Synthesis","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":6,"model":"MetaPose (S1+S2/SS)","metrics":{"Average MPJPE (mm)":"56"},"uses_additional_data":false,"paper_date":"2021-08-10","paper":"/paper/metapose-fast-3d-pose-from-multiple-views","paper_url":"https://arxiv.org/abs/2108.04869v2","paper_title":"MetaPose: Fast 3D Pose from Multiple Views without 3D Supervision","code":"https://github.com/google-research/google-research/tree/master/metapose","n_code_links":1,"syntology":null},{"rank_in_archive_order":7,"model":"PoseAug","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"56.7","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2021-05-06","paper":"/paper/poseaug-a-differentiable-pose-augmentation","paper_url":"https://arxiv.org/abs/2105.02465v1","paper_title":"PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation","code":"https://github.com/jfzhang95/PoseAug","n_code_links":1,"syntology":null},{"rank_in_archive_order":8,"model":"Non-Local Latent Relation Distillation","metrics":{"Average MPJPE (mm)":"57.6","PA-MPJPE":"48.2"},"uses_additional_data":false,"paper_date":"2022-04-05","paper":"/paper/non-local-latent-relation-distillation-for-1","paper_url":"https://arxiv.org/abs/2204.01971v2","paper_title":"Non-Local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":9,"model":"Uncertainty-Aware Adaptation","metrics":{"Average MPJPE (mm)":"59.4","PA-MPJPE":"49.6"},"uses_additional_data":false,"paper_date":"2022-03-29","paper":"/paper/uncertainty-aware-adaptation-for-self","paper_url":"https://arxiv.org/abs/2203.15293v1","paper_title":"Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":10,"model":"GeoRep (semi-supervised)","metrics":{"Average MPJPE (mm)":"59.7"},"uses_additional_data":false,"paper_date":"2020-03-17","paper":"/paper/weakly-supervised-3d-human-pose-learning-via","paper_url":"https://arxiv.org/abs/2003.07581v1","paper_title":"Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":11,"model":"EpipolarPose (SS + RU)","metrics":{"Average MPJPE (mm)":"60.56"},"uses_additional_data":false,"paper_date":"2019-03-06","paper":"/paper/self-supervised-learning-of-3d-human-pose","paper_url":"http://arxiv.org/abs/1903.02330v2","paper_title":"Self-Supervised Learning of 3D Human Pose using Multi-view Geometry","code":"https://github.com/mkocabas/EpipolarPose","n_code_links":1,"syntology":null},{"rank_in_archive_order":12,"model":"2D-3D Lifting self-supervised","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"62.0","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2021-08-17","paper":"/paper/self-supervised-3d-human-pose-estimation-with","paper_url":"https://arxiv.org/abs/2108.07777v1","paper_title":"Self-Supervised 3D Human Pose Estimation with Multiple-View Geometry","code":"https://github.com/vru2020/Pose_3D","n_code_links":1,"syntology":null},{"rank_in_archive_order":13,"model":"Li et al.","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"62.9","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2020-06-14","paper":"/paper/cascaded-deep-monocular-3d-human-pose-1","paper_url":"https://arxiv.org/abs/2006.07778v3","paper_title":"Cascaded deep monocular 3D human pose estimation with evolutionary training data","code":"https://github.com/Nicholasli1995/EvoSkeleton","n_code_links":1,"syntology":{"n_ran":2,"n_unverified":0,"n_samples":2,"n_pointer_only_licence":0}},{"rank_in_archive_order":14,"model":"TriPose","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"62.9","Number of Frames Per View":"27","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2021-05-14","paper":"/paper/tripose-a-weakly-supervised-3d-human-pose","paper_url":"https://arxiv.org/abs/2105.06599v1","paper_title":"TriPose: A Weakly-Supervised 3D Human Pose Estimation via Triangulation from Video","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":15,"model":"Pavllo et al.","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"64.7","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2018-11-28","paper":"/paper/3d-human-pose-estimation-in-video-with","paper_url":"http://arxiv.org/abs/1811.11742v2","paper_title":"3D human pose estimation in video with temporal convolutions and semi-supervised training","code":"https://github.com/open-mmlab/mmpose","n_code_links":10,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":16,"model":"Triangulation","metrics":{"Average MPJPE (mm)":"64.7","PA-MPJPE":"52.1"},"uses_additional_data":false,"paper_date":"2022-03-29","paper":"/paper/on-triangulation-as-a-form-of-self","paper_url":"https://arxiv.org/abs/2203.15865v3","paper_title":"On Triangulation as a Form of Self-Supervision for 3D Human Pose Estimation","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":17,"model":"EpipolarPose (S1)","metrics":{"Average MPJPE (mm)":"65.35"},"uses_additional_data":false,"paper_date":"2019-03-06","paper":"/paper/self-supervised-learning-of-3d-human-pose","paper_url":"http://arxiv.org/abs/1903.02330v2","paper_title":"Self-Supervised Learning of 3D Human Pose using Multi-view Geometry","code":"https://github.com/mkocabas/EpipolarPose","n_code_links":1,"syntology":null},{"rank_in_archive_order":18,"model":"GeoRep","metrics":{"Average MPJPE (mm)":"67.4"},"uses_additional_data":false,"paper_date":"2020-03-17","paper":"/paper/weakly-supervised-3d-human-pose-learning-via","paper_url":"https://arxiv.org/abs/2003.07581v1","paper_title":"Weakly-Supervised 3D Human Pose Learning via Multi-view Images in the Wild","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":19,"model":"CanonPose","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"74.3","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2020-11-30","paper":"/paper/canonpose-self-supervised-monocular-3d-human","paper_url":"https://arxiv.org/abs/2011.14679v1","paper_title":"CanonPose: Self-Supervised Monocular 3D Human Pose Estimation in the Wild","code":"https://github.com/bastianwandt/CanonPose","n_code_links":1,"syntology":null},{"rank_in_archive_order":20,"model":"EpipolarPose (self-supervised)","metrics":{"Average MPJPE (mm)":"76.6"},"uses_additional_data":false,"paper_date":"2019-03-06","paper":"/paper/self-supervised-learning-of-3d-human-pose","paper_url":"http://arxiv.org/abs/1903.02330v2","paper_title":"Self-Supervised Learning of 3D Human Pose using Multi-view Geometry","code":"https://github.com/mkocabas/EpipolarPose","n_code_links":1,"syntology":null},{"rank_in_archive_order":21,"model":"Wang et al.","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"83.0"},"uses_additional_data":false,"paper_date":"2019-08-18","paper":"/paper/distill-knowledge-from-nrsfm-for-weakly","paper_url":"https://arxiv.org/abs/1908.06377v1","paper_title":"Distill Knowledge from NRSfM for Weakly Supervised 3D Pose Learning","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":22,"model":"Tome et al.","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"88.4","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2017-01-01","paper":"/paper/lifting-from-the-deep-convolutional-3d-pose","paper_url":"http://arxiv.org/abs/1701.00295v4","paper_title":"Lifting from the Deep: Convolutional 3D Pose Estimation from a Single Image","code":"https://github.com/DenisTome/Lifting-from-the-Deep-release","n_code_links":11,"syntology":null},{"rank_in_archive_order":23,"model":"Li et al.","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"88.8","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2019-10-01","paper":"/paper/on-boosting-single-frame-3d-human-pose","paper_url":"http://openaccess.thecvf.com/content_ICCV_2019/html/Li_On_Boosting_Single-Frame_3D_Human_Pose_Estimation_via_Monocular_Videos_ICCV_2019_paper.html","paper_title":"On Boosting Single-Frame 3D Human Pose Estimation via Monocular Videos","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":24,"model":"RepNet","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"89.9","Number of Frames Per View":"1","Number of Views":"1"},"uses_additional_data":false,"paper_date":"2019-02-26","paper":"/paper/repnet-weakly-supervised-training-of-an","paper_url":"http://arxiv.org/abs/1902.09868v2","paper_title":"RepNet: Weakly Supervised Training of an Adversarial Reprojection Network for 3D Human Pose Estimation","code":"https://github.com/bastianwandt/RepNet","n_code_links":1,"syntology":null},{"rank_in_archive_order":25,"model":"self-supervised mocap","metrics":{"Average MPJPE (mm)":"98.4"},"uses_additional_data":false,"paper_date":"2017-12-04","paper":"/paper/self-supervised-learning-of-motion-capture","paper_url":"http://arxiv.org/abs/1712.01337v1","paper_title":"Self-supervised Learning of Motion Capture","code":"https://github.com/chingswy/HumanPoseMemo","n_code_links":1,"syntology":null},{"rank_in_archive_order":26,"model":"HW-HuP","metrics":{"Average MPJPE (mm)":"104.1","PA-MPJPE":"50.4"},"uses_additional_data":false,"paper_date":"2021-05-23","paper":"/paper/heuristic-weakly-supervised-3d-human-pose","paper_url":"https://arxiv.org/abs/2105.10996v3","paper_title":"Heuristic Weakly Supervised 3D Human Pose Estimation","code":"https://github.com/ostadabbas/hw-hup","n_code_links":2,"syntology":null},{"rank_in_archive_order":27,"model":"Pavlakos et al.","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"110.7"},"uses_additional_data":false,"paper_date":"2019-10-24","paper":"/paper/texturepose-supervising-human-mesh-estimation-1","paper_url":"https://arxiv.org/abs/1910.11322v1","paper_title":"TexturePose: Supervising Human Mesh Estimation with Texture Consistency","code":"https://github.com/geopavlakos/TexturePose","n_code_links":1,"syntology":null},{"rank_in_archive_order":28,"model":"Pavlakos et al.","metrics":{"3D Annotations":"No","Average MPJPE (mm)":"118.4"},"uses_additional_data":false,"paper_date":"2017-04-16","paper":"/paper/harvesting-multiple-views-for-marker-less-3d","paper_url":"http://arxiv.org/abs/1704.04793v1","paper_title":"Harvesting Multiple Views for Marker-less 3D Human Pose Annotations","code":null,"n_code_links":0,"syntology":null},{"rank_in_archive_order":29,"model":"Rhodin et al.","metrics":{"3D Annotations":"S1","Average MPJPE (mm)":"131.7"},"uses_additional_data":false,"paper_date":"2018-04-03","paper":"/paper/unsupervised-geometry-aware-representation","paper_url":"http://arxiv.org/abs/1804.01110v1","paper_title":"Unsupervised Geometry-Aware Representation for 3D Human Pose Estimation","code":"https://github.com/hrhodin/UnsupervisedGeometryAwareRepresentationLearning","n_code_links":2,"syntology":null},{"rank_in_archive_order":30,"model":"Kocabas et al.","metrics":{"3D Annotations":"S1","Number of Frames Per View":"1","Number of Views":"2"},"uses_additional_data":false,"paper_date":"2019-03-06","paper":"/paper/self-supervised-learning-of-3d-human-pose","paper_url":"http://arxiv.org/abs/1903.02330v2","paper_title":"Self-Supervised Learning of 3D Human Pose using Multi-view Geometry","code":"https://github.com/mkocabas/EpipolarPose","n_code_links":1,"syntology":null},{"rank_in_archive_order":31,"model":"PoseAug","metrics":{"3D Annotations":"S1","Number of Frames Per View":"1"},"uses_additional_data":false,"paper_date":"2021-05-06","paper":"/paper/poseaug-a-differentiable-pose-augmentation","paper_url":"https://arxiv.org/abs/2105.02465v1","paper_title":"PoseAug: A Differentiable Pose Augmentation Framework for 3D Human Pose Estimation","code":"https://github.com/jfzhang95/PoseAug","n_code_links":1,"syntology":null},{"rank_in_archive_order":32,"model":"VideoPose3D (T=243)","metrics":{"Number of Frames Per View":"243"},"uses_additional_data":false,"paper_date":"2018-11-28","paper":"/paper/3d-human-pose-estimation-in-video-with","paper_url":"http://arxiv.org/abs/1811.11742v2","paper_title":"3D human pose estimation in video with temporal convolutions and semi-supervised training","code":"https://github.com/open-mmlab/mmpose","n_code_links":10,"syntology":{"n_ran":0,"n_unverified":2,"n_samples":2,"n_pointer_only_licence":2}},{"rank_in_archive_order":33,"model":"Kanzawa et al.","metrics":{"3D Annotations":"No"},"uses_additional_data":false,"paper_date":"2017-12-18","paper":"/paper/end-to-end-recovery-of-human-shape-and-pose","paper_url":"http://arxiv.org/abs/1712.06584v2","paper_title":"End-to-end Recovery of Human Shape and Pose","code":"https://github.com/open-mmlab/mmpose","n_code_links":10,"syntology":null}],"since_archive":{"claim":"Results that newer papers report for their own method, placed here by Syntology. A model pointed at the cell in the paper's own table; the number was read from that cell and checked against this leaderboard's metric, dataset, split and scale; an independent check that saw this leaderboard's other rows and every other leaderboard on the same dataset accepted it. Not reviewed by the paper's authors or by the archive's editors, and not ranked against the archive rows.","extraction_file_present":true,"measurement":{"test_papers":883,"papers_with_output":881,"judged_true":108,"judged":110,"wilson95_lower":0.9361,"measured_on":"2026-09-24","frozen_commit":"0e3de0df94"},"measurement_note":"blind adjudication of accepted entries on a held-out split of archive papers, rules frozen before the test","coverage":{"sentence":"Syntology has checked 6,264 of the 9,581 papers on this site that are newer than the archive; results from the others appear after they are checked.","complete":false,"papers_newer_than_archive":9581,"papers_checked":6264,"papers_extracted_not_yet_verified":0,"boards_without_verdict":2,"papers_not_yet_extracted":3316},"order":"newest first by month (arXiv date, else the arXiv-id month), then arXiv id descending","columns":[],"entries":[]},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per row: N of M harvested code samples from that row's paper executed on a synthesized fixture; the other M-N are unverified. Not a reproduction of the row's number; not a correctness claim. n_pointer_only_licence counts samples the site points at rather than redistributes (a licence axis, independent of ran/unverified).","rows_with_graph_line":6,"rows_with_any_sample_ran":4,"distinct_papers_with_graph_line":4,"distinct_papers_with_any_sample_ran":3,"samples_over_distinct_papers":{"n_ran":5,"n_unverified":7,"n_samples":12,"n_pointer_only_licence":7,"note":"each paper (arXiv id) counted once, however many rows it is behind; this is the page-level figure"},"samples_row_weighted":{"n_ran":7,"n_unverified":12,"n_samples":19,"n_pointer_only_licence":14,"note":"row-weighted: a paper behind several rows is counted once per row; inflated relative to samples_over_distinct_papers by design, kept for readers summing the per-row syntology blocks"}}}