{"url":"/dataset/msra-hand","name":"MSRA Hand","full_name":"MSRA Hand","description_markdown":"**MSRA Hand**s is a dataset for hand tracking. In total 6 subjects' right hands are captured using Intel's Creative Interactive Gesture Camera. Each subject is asked to make various rapid gestures in a 400-frame video sequence. To account for different hand sizes, a global hand model scale is specified for each subject: 1.1, 1.0, 0.9, 0.95, 1.1, 1.0 for subject 1~6, respectively.\nThe camera intrinsic parameters are: principle point = image center(160, 120), focal length = 241.42. The depth image is 320x240, each *.bin file stores the depth pixel values in row scanning order, which are 320*240 floats. The unit is millimeters. The bin file is binary and needs to be opened with std::ios::binary flag.\njoint.txt file stores 400 frames x 21 hand joints per frame. Each line has 3 * 21 = 63 floats for 21 3D points in (x, y, z) coordinates. The 21 hand joints are: wrist, index_mcp, index_pip, index_dip, index_tip, middle_mcp, middle_pip, middle_dip, middle_tip, ring_mcp, ring_pip, ring_dip, ring_tip, little_mcp, little_pip, little_dip, little_tip, thumb_mcp, thumb_pip, thumb_dip, thumb_tip.\nThe corresponding *.jpg file is just for visualization of depth and ground truth joints.\n\nSource: [https://jimmysuen.github.io/txt/cvpr14_MSRAHandTrackingDB_readme.txt](https://jimmysuen.github.io/txt/cvpr14_MSRAHandTrackingDB_readme.txt)\nImage Source: [https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Qian_Realtime_and_Robust_2014_CVPR_paper.pdf](https://www.cv-foundation.org/openaccess/content_cvpr_2014/papers/Qian_Realtime_and_Robust_2014_CVPR_paper.pdf)","description_withheld":null,"homepage":"https://jimmysuen.github.io/","introduced_date":"2014-01-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/realtime-and-robust-hand-tracking-from-depth","title":"Realtime and Robust Hand Tracking from Depth","first_author":"Chen Qian","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Pose Estimation","url":"/task/pose-estimation","datasets_with_task":"/datasets/task/pose-estimation"},{"name":"Hand Pose Estimation","url":"/task/hand-pose-estimation","datasets_with_task":"/datasets/task/hand-pose-estimation"},{"name":"Stochastic Optimization","url":"/task/stochastic-optimization","datasets_with_task":"/datasets/task/stochastic-optimization"}],"languages":[],"variants":["MSRA Hands","MSRA Hand"],"data_loaders":[],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hand-pose-estimation-on-msra-hands","task":"Hand Pose Estimation","dataset_variant":"MSRA Hands","rows":11,"metrics":["Average 3D Error"],"first_row_in_archive_order":{"model":"TriHorn-Net","paper":"/paper/trihorn-net-a-model-for-accurate-depth-based","metrics":{"Average 3D Error":"7.13"},"code_links":[{"title":"mrezaei92/TriHorn-Net","url":"https://github.com/mrezaei92/TriHorn-Net"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/pushing-the-envelope-for-depth-based-semi","title":"Pushing the Envelope for Depth-Based Semi-Supervised 3D Hand Pose Estimation with Consistency Training","date":"2023-03-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/trihorn-net-a-model-for-accurate-depth-based","title":"TriHorn-Net: A Model for Accurate Depth-Based 3D Hand Pose Estimation","date":"2022-06-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/handfoldingnet-a-3d-hand-pose-estimation","title":"HandFoldingNet: A 3D Hand Pose Estimation Network Using Multiscale-Feature Guided Folding of a 2D Hand Skeleton","date":"2021-08-12","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":2,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/awr-adaptive-weighting-regression-for-3d-hand","title":"AWR: Adaptive Weighting Regression for 3D Hand Pose Estimation","date":"2020-07-19","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":5,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/pixel-wise-regression-3d-hand-pose-estimation","title":"Pixel-wise Regression: 3D Hand Pose Estimation via Spatial-form Representation and Differentiable Decoder","date":"2019-05-06","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/shpr-net-deep-semantic-hand-pose-regression","title":"SHPR-Net: Deep Semantic Hand Pose Regression From Point Clouds","date":"2018-08-06","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/dense-3d-regression-for-hand-pose-estimation","title":"Dense 3D Regression for Hand Pose Estimation","date":"2017-11-24","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/v2v-posenet-voxel-to-voxel-prediction-network","title":"V2V-PoseNet: Voxel-to-Voxel Prediction Network for Accurate 3D Hand and Human Pose Estimation from a Single Depth Map","date":"2017-11-20","rows_on_this_dataset":1,"code_links":5,"syntology":null},{"paper":"/paper/deepprior-improving-fast-and-accurate-3d-hand","title":"DeepPrior++: Improving Fast and Accurate 3D Hand Pose Estimation","date":"2017-08-28","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/pose-guided-structured-region-ensemble","title":"Pose Guided Structured Region Ensemble Network for Cascaded Hand Pose Estimation","date":"2017-08-11","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/region-ensemble-network-improving","title":"Region Ensemble Network: Improving Convolutional Network for Hand Pose Estimation","date":"2017-02-08","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":2,"samples_harvested":8,"samples_ran":7,"samples_unverified":1,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"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."}