{"url":"/dataset/florence3d","name":"Florence3D","full_name":null,"description_markdown":"The dataset collected at the University of Florence during 2012, has been captured using a Kinect camera. It includes 9 activities: wave, drink from a bottle, answer phone,clap, tight lace, sit down, stand up, read watch, bow. During acquisition, 10 subjects were asked to perform the above actions for 2/3 times. This resulted in a total of 215 activity samples.\r\n\r\nSource: [https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/](https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/)\r\nImage Source: [https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/](https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/)","description_withheld":null,"homepage":"https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/","introduced_date":"2013-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"Recognizing Actions from Depth Cameras as Weakly Aligned Multi-part Bag-of-Poses","first_author":null,"url":"https://doi.org/10.1109/CVPRW.2013.77"},"license":{"name":"Custom (research-only, non-commercial)","url":"https://www.micc.unifi.it/resources/datasets/florence-3d-actions-dataset/"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"Action Recognition","url":"/task/action-recognition-in-videos","datasets_with_task":"/datasets/task/action-recognition-in-videos"},{"name":"Temporal Action Localization","url":"/task/action-recognition","datasets_with_task":"/datasets/task/action-recognition"},{"name":"3D Action Recognition","url":"/task/3d-human-action-recognition","datasets_with_task":"/datasets/task/3d-human-action-recognition"},{"name":"Skeleton Based Action Recognition","url":"/task/skeleton-based-action-recognition","datasets_with_task":"/datasets/task/skeleton-based-action-recognition"}],"languages":[],"variants":["Florence 3D","Florence3D"],"data_loaders":[],"num_papers_in_archive":18,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/skeleton-based-action-recognition-on-florence","task":"Skeleton Based Action Recognition","dataset_variant":"Florence 3D","rows":7,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Deep STGC_K","paper":"/paper/spatio-temporal-graph-convolution-for","metrics":{"Accuracy":"99.1%"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/tensor-representations-for-action-recognition","title":"Tensor Representations for Action Recognition","date":"2020-12-28","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/subspace-clustering-for-action-recognition","title":"Subspace Clustering for Action Recognition with Covariance Representations and Temporal Pruning","date":"2020-06-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/generalized-graph-convolutional-networks-for","title":"Optimized Skeleton-based Action Recognition via Sparsified Graph Regression","date":"2018-11-29","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/spatio-temporal-graph-convolution-for","title":"Spatio-Temporal Graph Convolution for Skeleton Based Action Recognition","date":"2018-02-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rolling-rotations-for-recognizing-human-1","title":"Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data","date":"2016-06-27","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/human-action-recognition-by-representing-3d-1","title":"Human Action Recognition by Representing 3D Skeletons as Points in a Lie Group","date":"2014-06-23","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."}