{"url":"/dataset/florence","name":"Florence","full_name":"Florence 3D Faces","description_markdown":"The **Florence** 3D faces dataset consists of:\r\n\r\n* High-resolution 3D scans of human faces from many subjects.\r\n* Several video sequences of varying resolution, conditions and zoom level for each subject.\r\nEach subject is recorded in the following situations:\r\n* In a controlled setting in HD video.\r\n* In a less-constrained (but still indoor) setting using a standard, PTZ surveillance camera.\r\n* In an unconstrained, outdoor environment under challenging recording conditions.\r\n\r\nSource: [https://www.micc.unifi.it/resources/datasets/florence-3d-faces/](https://www.micc.unifi.it/resources/datasets/florence-3d-faces/)\r\nImage Source: [https://www.micc.unifi.it/resources/datasets/florence-3d-faces/](https://www.micc.unifi.it/resources/datasets/florence-3d-faces/)","description_withheld":null,"homepage":"https://www.micc.unifi.it/resources/datasets/florence-3d-faces/","introduced_date":"2011-01-01","introduced_date_note":null,"introduced_by":{"paper":null,"title":"The florence 2D/3D hybrid face dataset","first_author":null,"url":"https://doi.org/10.1145/2072572.2072597"},"license":{"name":"Custom","url":"https://drive.google.com/file/d/12eDn3l4EaTrDATgNVAH7frHZt4d4mr78/view"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"3D Face Reconstruction","url":"/task/3d-face-reconstruction","datasets_with_task":"/datasets/task/3d-face-reconstruction"}],"languages":[],"variants":["Florence"],"data_loaders":[],"num_papers_in_archive":40,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-face-reconstruction-on-florence","task":"3D Face Reconstruction","dataset_variant":"Florence","rows":16,"metrics":["Mean NME ","Average 3D Error","RMSE Cooperative","RMSE Indoor","RMSE Outdoor","Mean NME"],"first_row_in_archive_order":{"model":"PRN","paper":"/paper/joint-3d-face-reconstruction-and-dense","metrics":{"Mean NME ":"3.7551%"},"code_links":[{"title":"YadiraF/PRNet","url":"https://github.com/YadiraF/PRNet"},{"title":"jimmy0087/faceai-master","url":"https://github.com/jimmy0087/faceai-master"},{"title":"minoring/PRNet","url":"https://github.com/minoring/PRNet"},{"title":"heathentw/prnet-tf2","url":"https://github.com/heathentw/prnet-tf2"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/asm-adaptive-skinning-model-for-high-quality","title":"ASM: Adaptive Skinning Model for High-Quality 3D Face Modeling","date":"2023-04-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/3d-face-reconstruction-with-dense-landmarks","title":"3D face reconstruction with dense landmarks","date":"2022-04-06","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/towards-fast-accurate-and-stable-3d-dense-1","title":"Towards Fast, Accurate and Stable 3D Dense Face Alignment","date":"2020-09-21","rows_on_this_dataset":1,"code_links":3,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":6,"samples_unverified":4,"pointer_only_for_licence":3,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/accurate-3d-face-reconstruction-with-weakly","title":"Accurate 3D Face Reconstruction with Weakly-Supervised Learning: From Single Image to Image Set","date":"2019-03-20","rows_on_this_dataset":2,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":36,"samples_ran":11,"samples_unverified":25,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/ganfit-generative-adversarial-network-fitting","title":"GANFIT: Generative Adversarial Network Fitting for High Fidelity 3D Face Reconstruction","date":"2019-02-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/unsupervised-training-for-3d-morphable-model","title":"Unsupervised Training for 3D Morphable Model Regression","date":"2018-06-15","rows_on_this_dataset":2,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":13,"samples_ran":0,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/joint-3d-face-reconstruction-and-dense","title":"Joint 3D Face Reconstruction and Dense Alignment with Position Map Regression Network","date":"2018-03-21","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":10,"samples_ran":0,"samples_unverified":10,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/large-pose-3d-face-reconstruction-from-a","title":"Large Pose 3D Face Reconstruction from a Single Image via Direct Volumetric CNN Regression","date":"2017-03-22","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/3d-face-morphable-models-in-the-wild","title":"3D Face Morphable Models \"In-the-Wild\"","date":"2017-01-19","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/regressing-robust-and-discriminative-3d","title":"Regressing Robust and Discriminative 3D Morphable Models with a very Deep Neural Network","date":"2016-12-15","rows_on_this_dataset":2,"code_links":5,"syntology":null},{"paper":"/paper/automated-3d-face-reconstruction-from","title":"Automated 3D Face Reconstruction From Multiple Images Using Quality Measures","date":"2016-06-01","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/face-alignment-across-large-poses-a-3d","title":"Face Alignment Across Large Poses: A 3D Solution","date":"2015-11-23","rows_on_this_dataset":1,"code_links":0,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":4,"samples_harvested":69,"samples_ran":17,"samples_unverified":52,"pointer_only_for_licence":3,"papers_with_no_sample_that_ran":2,"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."}