{"url":"/dataset/agora","name":"AGORA","full_name":null,"description_markdown":"AGORA is a synthetic human dataset with high realism and accurate ground truth. It consists of around 14K training and 3K test images by rendering between 5 and 15 people per image using either image-based lighting or rendered 3D environments, taking care to make the images physically plausible and photoreal. In total, AGORA contains 173K individual person crops.\r\nAGORA provides (1) SMPL/SMPL-X parameters and (2) segmentation masks for each subject in images.","description_withheld":null,"homepage":"https://agora.is.tue.mpg.de","introduced_date":"2021-04-29","introduced_date_note":null,"introduced_by":{"paper":"/paper/agora-avatars-in-geography-optimized-for","title":"AGORA: Avatars in Geography Optimized for Regression Analysis","first_author":"Priyanka Patel","url":null},"license":{"name":"Custom (non-commercial)","url":"https://agora.is.tue.mpg.de/license.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"3D Human Pose Estimation","url":"/task/3d-human-pose-estimation","datasets_with_task":"/datasets/task/3d-human-pose-estimation"},{"name":"2D Human Pose Estimation","url":"/task/2d-human-pose-estimation","datasets_with_task":"/datasets/task/2d-human-pose-estimation"},{"name":"3D Hand Pose Estimation","url":"/task/3d-hand-pose-estimation","datasets_with_task":"/datasets/task/3d-hand-pose-estimation"},{"name":"3D Human Reconstruction","url":"/task/3d-human-reconstruction","datasets_with_task":"/datasets/task/3d-human-reconstruction"},{"name":"3D Multi-Person Pose Estimation","url":"/task/3d-multi-person-pose-estimation","datasets_with_task":"/datasets/task/3d-multi-person-pose-estimation"},{"name":"3D Multi-Person Mesh Recovery","url":"/task/3d-multi-person-mesh-recovery","datasets_with_task":"/datasets/task/3d-multi-person-mesh-recovery"},{"name":"3D Human Shape Estimation","url":"/task/3d-human-shape-estimation","datasets_with_task":"/datasets/task/3d-human-shape-estimation"},{"name":"Monocular 3D Human Pose Estimation","url":"/task/monocular-3d-human-pose-estimation","datasets_with_task":"/datasets/task/monocular-3d-human-pose-estimation"}],"languages":[],"variants":["AGORA"],"data_loaders":[{"repo":"https://github.com/pixelite1201/agora_evaluation","url":"https://github.com/pixelite1201/agora_evaluation","frameworks":[]}],"num_papers_in_archive":68,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-human-pose-estimation-on-agora","task":"3D Human Pose Estimation","dataset_variant":"AGORA","rows":11,"metrics":["B-NMVE","B-NMJE","B-MVE","B-MPJPE"],"first_row_in_archive_order":{"model":"NIKI (Twist-and-Swing)","paper":"/paper/niki-neural-inverse-kinematics-with","metrics":{"B-MPJPE":"67.3","B-MVE":"63.9","B-NMJE":"74","B-NMVE":"70.2"},"code_links":[{"title":"jeff-sjtu/niki","url":"https://github.com/jeff-sjtu/niki"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-multi-person-mesh-recovery-on-agora","task":"3D Multi-Person Mesh Recovery","dataset_variant":"AGORA","rows":7,"metrics":["FB-NMVE","B-NMVE","FB-NMJE","B-NMJE","FB-MVE","B-MVE","F-MVE","LH/RH-MVE","FB-MPJPE","B-MPJPE","F-MPJPE","LH/RH-MPJPE"],"first_row_in_archive_order":{"model":"AIOS","paper":"/paper/aios-all-in-one-stage-expressive-human-pose","metrics":{"FB-NMVE":"97.8"},"code_links":[{"title":"ttxskk/AiOS","url":"https://github.com/ttxskk/AiOS"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-human-reconstruction-on-agora-1","task":"3D Human Reconstruction","dataset_variant":"AGORA","rows":5,"metrics":["FB-NMVE","B-NMVE","FB-NMJE","B-NMJE","FB-MVE","B-MVE","F-MVE","LH/RH-MVE","FB-MPJPE","B-MPJPE","F-MPJPE","LH/RH-MPJPE","PA-MPVPE"],"first_row_in_archive_order":{"model":"HybrIK-X","paper":"/paper/hybrik-x-hybrid-analytical-neural-inverse","metrics":{"FB-MPJPE":"107.6","FB-MVE":"112.1","FB-NMJE":"115.7","FB-NMVE":"120.5"},"code_links":[{"title":"jeffffffli/HybrIK","url":"https://github.com/jeffffffli/HybrIK"},{"title":"Jeff-sjtu/HybrIK","url":"https://github.com/Jeff-sjtu/HybrIK"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/3d-multi-person-pose-estimation-on-agora","task":"3D Multi-Person Pose Estimation","dataset_variant":"AGORA","rows":4,"metrics":["B-NMVE","B-NMJE","B-MVE","B-MPJPE"],"first_row_in_archive_order":{"model":"SPEC","paper":"/paper/spec-seeing-people-in-the-wild-with-an","metrics":{"B-MPJPE":"112.3","B-MVE":"106.5","B-NMJE":"133.7","B-NMVE":"126.8"},"code_links":[{"title":"mkocabas/SPEC","url":"https://github.com/mkocabas/SPEC"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/aios-all-in-one-stage-expressive-human-pose","title":"AiOS: All-in-One-Stage Expressive Human Pose and Shape Estimation","date":"2024-03-26","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/multi-hmr-multi-person-whole-body-human-mesh","title":"Multi-HMR: Multi-Person Whole-Body Human Mesh Recovery in a Single Shot","date":"2024-02-22","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":8,"samples_unverified":1,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/w-hmr-human-mesh-recovery-in-world-space-with","title":"W-HMR: Monocular Human Mesh Recovery in World Space with Weak-Supervised Calibration","date":"2023-11-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/smpler-x-scaling-up-expressive-human-pose-and","title":"SMPLer-X: Scaling Up Expressive Human Pose and Shape Estimation","date":"2023-09-29","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/niki-neural-inverse-kinematics-with","title":"NIKI: Neural Inverse Kinematics with Invertible Neural Networks for 3D Human Pose and Shape Estimation","date":"2023-05-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":1,"samples_unverified":1,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hybrik-x-hybrid-analytical-neural-inverse","title":"HybrIK-X: Hybrid Analytical-Neural Inverse Kinematics for Whole-body Mesh Recovery","date":"2023-04-12","rows_on_this_dataset":3,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":0,"samples_unverified":8,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/one-stage-3d-whole-body-mesh-recovery-with","title":"One-Stage 3D Whole-Body Mesh Recovery with Component Aware Transformer","date":"2023-03-28","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-person-3d-pose-and-shape-estimation-via","title":"Multi-Person 3D Pose and Shape Estimation via Inverse Kinematics and Refinement","date":"2022-10-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pymaf-x-towards-well-aligned-full-body-model","title":"PyMAF-X: Towards Well-aligned Full-body Model Regression from Monocular Images","date":"2022-07-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/spec-seeing-people-in-the-wild-with-an","title":"SPEC: Seeing People in the Wild with an Estimated Camera","date":"2021-10-01","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/collaborative-regression-of-expressive-bodies","title":"Collaborative Regression of Expressive Bodies using Moderation","date":"2021-05-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pare-part-attention-regressor-for-3d-human","title":"PARE: Part Attention Regressor for 3D Human Body Estimation","date":"2021-04-17","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/3d-human-pose-and-shape-regression-with","title":"PyMAF: 3D Human Pose and Shape Regression with Pyramidal Mesh Alignment Feedback Loop","date":"2021-03-30","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/pose2pose-3d-positional-pose-guided-3d","title":"Accurate 3D Hand Pose Estimation for Whole-Body 3D Human Mesh Estimation","date":"2020-11-23","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/monocular-expressive-body-regression-through","title":"Monocular Expressive Body Regression through Body-Driven Attention","date":"2020-08-20","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/coherent-reconstruction-of-multiple-humans-1","title":"Coherent Reconstruction of Multiple Humans from a Single Image","date":"2020-06-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/learning-to-reconstruct-3d-human-pose-and","title":"Learning to Reconstruct 3D Human Pose and Shape via Model-fitting in the Loop","date":"2019-09-27","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/expressive-body-capture-3d-hands-face-and","title":"Expressive Body Capture: 3D Hands, Face, and Body from a Single Image","date":"2019-04-11","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/end-to-end-recovery-of-human-shape-and-pose","title":"End-to-end Recovery of Human Shape and Pose","date":"2017-12-18","rows_on_this_dataset":2,"code_links":10,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":5,"samples_harvested":24,"samples_ran":14,"samples_unverified":10,"pointer_only_for_licence":13,"papers_with_no_sample_that_ran":1,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}