{"url":"/dataset/shrec","name":"SHREC","full_name":"SHape REtrieval Contest","description_markdown":"The **SHREC** dataset contains 14 dynamic gestures performed by 28 participants (all participants are right handed) and captured by the Intel RealSense short range depth camera. Each gesture is performed between 1 and 10 times by each participant in two way: using one finger and the whole hand. Therefore, the dataset is composed by 2800 sequences captured. The depth image, with a resolution of 640x480, and the coordinates of 22 joints (both in the 2D depth image space and in the 3D world space) are saved for each frame of each sequence in the dataset.\n\nSource: [Exploiting Recurrent Neural Networks and Leap Motion Controller for Sign Language and Semaphoric Gesture Recognition](https://arxiv.org/abs/1803.10435)\nImage Source: [http://tosca.cs.technion.ac.il/book/shrec.html](http://tosca.cs.technion.ac.il/book/shrec.html)","description_withheld":null,"homepage":"http://tosca.cs.technion.ac.il/book/shrec.html","introduced_date":"2015-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"A comparison of 3D shape retrieval methods based on a large-scale benchmark supporting multimodal queries","first_author":null,"url":"https://doi.org/10.1016/j.cviu.2014.10.006"},"license":null,"modalities":[],"tasks":[{"name":"Skeleton Based Action Recognition","url":"/task/skeleton-based-action-recognition","datasets_with_task":"/datasets/task/skeleton-based-action-recognition"},{"name":"Gesture Recognition","url":"/task/gesture-recognition","datasets_with_task":"/datasets/task/gesture-recognition"},{"name":"Hand Gesture Recognition","url":"/task/hand-gesture-recognition","datasets_with_task":"/datasets/task/hand-gesture-recognition"},{"name":"3D Object Recognition","url":"/task/3d-object-recognition","datasets_with_task":"/datasets/task/3d-object-recognition"},{"name":"Point Cloud Super Resolution","url":"/task/point-cloud-super-resolution","datasets_with_task":"/datasets/task/point-cloud-super-resolution"}],"languages":[],"variants":["SHREC 2017 track on 3D Hand Gesture Recognition","SHREC15","DHG-14","DHG-28","SHREC 2017","SHREC11, Split16-4","SHREC"],"data_loaders":[],"num_papers_in_archive":32,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/hand-gesture-recognition-on-dhg-14","task":"Hand Gesture Recognition","dataset_variant":"DHG-14","rows":13,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"e2eET","paper":"/paper/real-time-hand-gesture-recognition","metrics":{"Accuracy":"95.83"},"code_links":[{"title":"outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion","url":"https://github.com/outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/hand-gesture-recognition-on-dhg-28","task":"Hand Gesture Recognition","dataset_variant":"DHG-28","rows":9,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"DSTSA-GCN","paper":"/paper/dstsa-gcn-advancing-skeleton-based-gesture","metrics":{"Accuracy":"93.57"},"code_links":[{"title":"HuCui2022/DSTSA-GCN_Gesture","url":"https://github.com/HuCui2022/DSTSA-GCN_Gesture"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/skeleton-based-action-recognition-on-shrec","task":"Skeleton Based Action Recognition","dataset_variant":"SHREC 2017 track on 3D Hand Gesture Recognition","rows":7,"metrics":["28 gestures accuracy","14 gestures accuracy","Speed  (FPS)","No. Parameters"],"first_row_in_archive_order":{"model":"RE-TCN","paper":"/paper/action-recognition-in-real-world-ambient","metrics":{"14 gestures accuracy":"99.85","28 gestures accuracy":"99.95","No. Parameters":"1.25"},"code_links":[{"title":"Gbouna/RE-TCN","url":"https://github.com/Gbouna/RE-TCN"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/hand-gesture-recognition-on-shrec-2017","task":"Hand Gesture Recognition","dataset_variant":"SHREC 2017","rows":4,"metrics":["14 Gestures Accuracy","28 Gestures Accuracy"],"first_row_in_archive_order":{"model":"e2eET","paper":"/paper/real-time-hand-gesture-recognition","metrics":{"14 Gestures Accuracy":"97.86","28 Gestures Accuracy":"95.36"},"code_links":[{"title":"outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion","url":"https://github.com/outsiders17711/e2eet-skeleton-based-hgr-using-data-level-fusion"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/hand-gesture-recognition-on-shrec-2017-track","task":"Hand Gesture Recognition","dataset_variant":"SHREC 2017 track on 3D Hand Gesture Recognition","rows":3,"metrics":["14 gestures 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Accuracy"],"first_row_in_archive_order":{"model":"MeshWalker (ours)","paper":"/paper/meshwalker-deep-mesh-understanding-by-random","metrics":{"Per-Class Accuracy":"98.6"},"code_links":[{"title":"AlonLahav/MeshWalker","url":"https://github.com/AlonLahav/MeshWalker"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/gesture-recognition-on-shrec-2017-track-on-3d","task":"Gesture Recognition","dataset_variant":"SHREC 2017 track on 3D Hand Gesture Recognition","rows":1,"metrics":["14 gestures accuracy"],"first_row_in_archive_order":{"model":"PointLSTM","paper":"/paper/an-efficient-pointlstm-for-point-clouds-based","metrics":{"14 gestures accuracy":"95.9"},"code_links":[{"title":"Blueprintf/pointlstm-gesture-recognition-pytorch","url":"https://github.com/Blueprintf/pointlstm-gesture-recognition-pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/action-recognition-in-real-world-ambient","title":"Action Recognition in Real-World Ambient Assisted Living Environment","date":"2025-03-29","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/dstsa-gcn-advancing-skeleton-based-gesture","title":"DSTSA-GCN: Advancing Skeleton-Based Gesture Recognition with Semantic-Aware Spatio-Temporal Topology Modeling","date":"2025-01-21","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/hierarchical-temporal-convolution-network","title":"Hierarchical Temporal Convolution Network:Towards Privacy-Centric Activity Recognition","date":"2024-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/real-time-hand-gesture-recognition","title":"Real-Time Hand Gesture Recognition: Integrating Skeleton-Based Data Fusion and Multi-Stream 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Recognition","date":"2020-06-01","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/lossless-compression-for-3dcnns-based-on","title":"Compressing 3DCNNs Based on Tensor Train Decomposition","date":"2019-12-08","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/make-skeleton-based-action-recognition-model-1","title":"Make Skeleton-based Action Recognition Model Smaller, Faster and Better","date":"2019-07-23","rows_on_this_dataset":3,"code_links":3,"syntology":null},{"paper":"/paper/construct-dynamic-graphs-for-hand-gesture","title":"Construct Dynamic Graphs for Hand Gesture Recognition via Spatial-Temporal Attention","date":"2019-07-20","rows_on_this_dataset":3,"code_links":1,"syntology":null},{"paper":"/paper/spatial-temporal-attention-res-tcn-for","title":"Spatial-Temporal Attention Res-TCN for Skeleton-Based Dynamic Hand Gesture Recognition","date":"2019-01-23","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/motion-feature-augmented-network-for-dynamic","title":"Motion feature augmented network for dynamic hand gesture recognition from skeletal data","date":"2019-01-10","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/deep-learning-for-hand-gesture-recognition-on","title":"Deep Learning for Hand Gesture Recognition on Skeletal Data","date":"2018-05-15","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pu-net-point-cloud-upsampling-network","title":"PU-Net: Point Cloud Upsampling Network","date":"2018-01-21","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/motion-feature-augmented-recurrent-neural","title":"Motion Feature Augmented Recurrent Neural Network for Skeleton-based Dynamic Hand Gesture Recognition","date":"2017-08-10","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/point-cloud-super-resolution-with-adversarial","title":"Point Cloud Super Resolution with Adversarial Residual Graph Networks","date":null,"rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"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."}