{"url":"/dataset/freihand","name":"FreiHAND","full_name":"FreiHAND","description_markdown":"**FreiHAND** is a 3D hand pose dataset which records different hand actions performed by 32 people. For each hand image, MANO-based 3D hand pose annotations are provided. It currently contains 32,560 unique training samples and 3960 unique samples for evaluation. The training samples are recorded with a green screen background allowing for background removal. In addition, it applies three different post processing strategies to training samples for data augmentation. However, these post processing strategies are not applied to evaluation samples.\r\n\r\nSource: [Knowledge as Priors: Cross-Modal Knowledge Generalizationfor Datasets without Superior Knowledge](https://arxiv.org/abs/2004.00176)\r\nImage Source: [https://lmb.informatik.uni-freiburg.de/resources/datasets/FreihandDataset.en.html](https://lmb.informatik.uni-freiburg.de/resources/datasets/FreihandDataset.en.html)","description_withheld":null,"homepage":"https://lmb.informatik.uni-freiburg.de/projects/freihand/","introduced_date":"2019-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/freihand-a-dataset-for-markerless-capture-of","title":"FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images","first_author":"Christian Zimmermann","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"https://lmb.informatik.uni-freiburg.de/resources/datasets/FreihandDataset.en.html"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"3D Hand Pose Estimation","url":"/task/3d-hand-pose-estimation","datasets_with_task":"/datasets/task/3d-hand-pose-estimation"}],"languages":[],"variants":["FreiHAND"],"data_loaders":[{"repo":"https://github.com/open-mmlab/mmpose","url":"https://github.com/open-mmlab/mmpose/blob/master/docs/tasks/2d_hand_keypoint.md#freihand-dataset","frameworks":["pytorch"]}],"num_papers_in_archive":125,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-hand-pose-estimation-on-freihand","task":"3D Hand Pose Estimation","dataset_variant":"FreiHAND","rows":33,"metrics":["PA-MPJPE","PA-MPVPE","PA-F@5mm","PA-F@15mm"],"first_row_in_archive_order":{"model":"ExtPose","paper":"/paper/extpose-robust-and-coherent-pose-estimation","metrics":{"PA-F@15mm":"0.993","PA-F@5mm":"0.823","PA-MPJPE":"4.9","PA-MPVPE":"5.1"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/extpose-robust-and-coherent-pose-estimation","title":"ExtPose: Robust and Coherent Pose Estimation by Extending ViTs","date":"2025-06-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/mmhmr-generative-masked-modeling-for-hand","title":"MMHMR: Generative Masked Modeling for Hand Mesh Recovery","date":"2024-12-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/handos-3d-hand-reconstruction-in-one-stage","title":"HandOS: 3D Hand Reconstruction in One Stage","date":"2024-12-02","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/wilor-end-to-end-3d-hand-localization-and","title":"WiLoR: End-to-end 3D Hand Localization and Reconstruction in-the-wild","date":"2024-09-18","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/hamba-single-view-3d-hand-reconstruction-with","title":"Hamba: Single-view 3D Hand Reconstruction with Graph-guided Bi-Scanning Mamba","date":"2024-07-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":11,"samples_ran":7,"samples_unverified":4,"pointer_only_for_licence":11,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/hhmr-holistic-hand-mesh-recovery-by-enhancing","title":"HHMR: Holistic Hand Mesh Recovery by Enhancing the Multimodal Controllability of Graph Diffusion Models","date":"2024-06-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/a-simple-baseline-for-efficient-hand-mesh","title":"A Simple Baseline for Efficient Hand Mesh Reconstruction","date":"2024-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":15,"samples_ran":1,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/reconstructing-hands-in-3d-with-transformers","title":"Reconstructing Hands in 3D with Transformers","date":"2023-12-08","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":6,"samples_ran":0,"samples_unverified":6,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/a-probabilistic-attention-model-with","title":"A Probabilistic Attention Model with Occlusion-aware Texture Regression for 3D Hand Reconstruction from a Single RGB Image","date":"2023-04-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sampling-is-matter-point-guided-3d-human-mesh-1","title":"Sampling is Matter: Point-guided 3D Human Mesh Reconstruction","date":"2023-04-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":8,"samples_ran":5,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/fastvit-a-fast-hybrid-vision-transformer","title":"FastViT: A Fast Hybrid Vision Transformer using Structural Reparameterization","date":"2023-03-24","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":1,"samples_unverified":4,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deformable-mesh-transformer-for-3d-human-mesh","title":"Deformable Mesh Transformer for 3D Human Mesh Recovery","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cross-attention-of-disentangled-modalities","title":"Cross-Attention of Disentangled Modalities for 3D Human Mesh Recovery with Transformers","date":"2022-07-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":16,"samples_ran":12,"samples_unverified":4,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/mobrecon-mobile-friendly-hand-mesh","title":"MobRecon: Mobile-Friendly Hand Mesh Reconstruction from Monocular Image","date":"2021-12-06","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":2,"samples_unverified":7,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/towards-accurate-alignment-in-real-time-3d","title":"Towards Accurate Alignment in Real-time 3D Hand-Mesh Reconstruction","date":"2021-09-03","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hand-image-understanding-via-deep-multi-task","title":"Hand Image Understanding via Deep Multi-Task Learning","date":"2021-07-24","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":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/self-supervised-3d-hand-pose-estimation-from","title":"PeCLR: Self-Supervised 3D Hand Pose Estimation from monocular RGB via Equivariant Contrastive Learning","date":"2021-06-10","rows_on_this_dataset":1,"code_links":2,"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/mesh-graphormer","title":"Mesh Graphormer","date":"2021-04-01","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/camera-space-hand-mesh-recovery-via-semantic","title":"Camera-Space Hand Mesh Recovery via Semantic Aggregation and Adaptive 2D-1D Registration","date":"2021-03-04","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sr-affine-high-quality-3d-hand-model","title":"I2UV-HandNet: Image-to-UV Prediction Network for Accurate and High-fidelity 3D Hand Mesh Modeling","date":"2021-02-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/end-to-end-human-pose-and-mesh-reconstruction","title":"End-to-End Human Pose and Mesh Reconstruction with Transformers","date":"2020-12-17","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/monocular-real-time-full-body-capture-with","title":"Monocular Real-time Full Body Capture with Inter-part Correlations","date":"2020-12-11","rows_on_this_dataset":1,"code_links":0,"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/pose2mesh-graph-convolutional-network-for-3d","title":"Pose2Mesh: Graph Convolutional Network for 3D Human Pose and Mesh Recovery from a 2D Human Pose","date":"2020-08-20","rows_on_this_dataset":1,"code_links":2,"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":1,"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/i2l-meshnet-image-to-lixel-prediction-network-1","title":"I2L-MeshNet: Image-to-Lixel Prediction Network for Accurate 3D Human Pose and Mesh Estimation from a Single RGB Image","date":"2020-08-09","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":4,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/weakly-supervised-mesh-convolutional-hand","title":"Weakly-Supervised Mesh-Convolutional Hand Reconstruction in the Wild","date":"2020-04-04","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":20,"samples_ran":6,"samples_unverified":14,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/freihand-a-dataset-for-markerless-capture-of","title":"FreiHAND: A Dataset for Markerless Capture of Hand Pose and Shape from Single RGB Images","date":"2019-09-10","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/learning-joint-reconstruction-of-hands-and","title":"Learning joint reconstruction of hands and manipulated objects","date":"2019-04-11","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/3d-hand-shape-and-pose-from-images-in-the","title":"3D Hand Shape and Pose from Images in the Wild","date":"2019-02-09","rows_on_this_dataset":1,"code_links":2,"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."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":12,"samples_harvested":101,"samples_ran":43,"samples_unverified":58,"pointer_only_for_licence":20,"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."}