{"url":"/dataset/dynamic-faust","name":"Dynamic FAUST","full_name":null,"description_markdown":"Dynamic FAUST extends the FAUST dataset to dynamic 4D data. It consists of high-resolution 4D scans of human subjects in motion, captured at 60 fps.\r\n\r\nSource: [Dynamic FAUST: Registering Human Bodies in Motion](/paper/dynamic-faust-registering-human-bodies-in)","description_withheld":null,"homepage":"http://dfaust.is.tue.mpg.de","introduced_date":null,"introduced_date_note":null,"introduced_by":{"paper":"/paper/dynamic-faust-registering-human-bodies-in","title":"Dynamic FAUST: Registering Human Bodies in Motion","first_author":"Federica Bogo","url":null},"license":{"name":"Custom (research-only, non-commercial)","url":"https://dfaust.is.tue.mpg.de/license"},"modalities":[{"name":"Videos","url":"/datasets/modality/videos"},{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"3D Reconstruction","url":"/task/3d-reconstruction","datasets_with_task":"/datasets/task/3d-reconstruction"},{"name":"3D Human Reconstruction","url":"/task/3d-human-reconstruction","datasets_with_task":"/datasets/task/3d-human-reconstruction"},{"name":"3D Shape Representation","url":"/task/3d-shape-representation","datasets_with_task":"/datasets/task/3d-shape-representation"}],"languages":[],"variants":["Dynamic FAUST"],"data_loaders":[{"repo":"https://github.com/rusty1s/pytorch_geometric","url":"https://pytorch-geometric.readthedocs.io/en/latest/modules/datasets.html","frameworks":["pytorch"]}],"num_papers_in_archive":28,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-human-reconstruction-on-dynamic-faust","task":"3D Human Reconstruction","dataset_variant":"Dynamic FAUST","rows":1,"metrics":["Volumetric IoU"],"first_row_in_archive_order":{"model":"RFNet-4D","paper":"/paper/rfnet-4d-joint-object-reconstruction-and-flow","metrics":{"Volumetric IoU":"85.47"},"code_links":[{"title":"hkust-vgd/rfnet-4d","url":"https://github.com/hkust-vgd/rfnet-4d"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rfnet-4d-joint-object-reconstruction-and-flow","title":"RFNet-4D++: Joint Object Reconstruction and Flow Estimation from 4D Point Clouds with Cross-Attention Spatio-Temporal Features","date":"2022-03-30","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-25T09:33:49+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."}