{"url":"/dataset/customhumans","name":"CustomHumans","full_name":null,"description_markdown":"CustomHumans is recorded by a multi-view photogrammetry system equipped with 53 RGB (12 Megapixels) and 53 (4 Megapixels) IR cameras. The resulting high-quality scan is composed of a 40K-face mesh alongside a 4K-resolution texture map. In addition to the high-quality scans, CustomHumans provides accurately registered SMPL-X parameters using a customized mesh registration pipeline. 80 participants are invited for the data capturing. Each of them is instructed to perform several movements, such as \"T-pose\", \"Hands Up'\", \"Squat'\", \"Turing head'', and \"Hand gestures\", in a 10-second long sequence (300 frames). 4-5 best-quality meshes in each sequence are selected as the data samples. In total, the dataset contains more than 600 high-quality scans with 120 different garments.","description_withheld":null,"homepage":"https://custom-humans.github.io/#download","introduced_date":"2023-04-28","introduced_date_note":null,"introduced_by":{"paper":"/paper/learning-locally-editable-virtual-humans","title":"Learning Locally Editable Virtual Humans","first_author":"Hsuan-I Ho","url":null},"license":{"name":"ETH","url":"https://custom-humans.ait.ethz.ch/"},"modalities":[{"name":"3D","url":"/datasets/modality/3d"}],"tasks":[{"name":"3D Human Reconstruction","url":"/task/3d-human-reconstruction","datasets_with_task":"/datasets/task/3d-human-reconstruction"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["CustomHumans"],"data_loaders":[{"repo":"https://github.com/custom-humans/editable-humans","url":"https://github.com/custom-humans/editable-humans","frameworks":["pytorch"]}],"num_papers_in_archive":15,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/3d-human-reconstruction-on-customhumans","task":"3D Human Reconstruction","dataset_variant":"CustomHumans","rows":8,"metrics":["Chamfer Distance P-to-S","Chamfer Distance S-to-P","Normal Consistency","f-Score"],"first_row_in_archive_order":{"model":"SiTH","paper":"/paper/sith-single-view-textured-human","metrics":{"Chamfer Distance P-to-S":"1.871","Chamfer Distance S-to-P":"2.045","Normal Consistency":"0.826","f-Score":"37.029"},"code_links":[{"title":"SiTH-Diffusion/SiTH","url":"https://github.com/SiTH-Diffusion/SiTH"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/sith-single-view-textured-human","title":"SiTH: Single-view Textured Human Reconstruction with Image-Conditioned Diffusion","date":"2023-11-27","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/high-fidelity-3d-human-digitization-from","title":"High-fidelity 3D Human Digitization from Single 2K Resolution Images","date":"2023-03-27","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/econ-explicit-clothed-humans-obtained-from","title":"ECON: Explicit Clothed humans Optimized via Normal integration","date":"2022-12-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fof-learning-fourier-occupancy-field-for","title":"FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction","date":"2022-06-05","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/icon-implicit-clothed-humans-obtained-from","title":"ICON: Implicit Clothed humans Obtained from Normals","date":"2021-12-16","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/pamir-parametric-model-conditioned-implicit","title":"PaMIR: Parametric Model-Conditioned Implicit Representation for Image-based Human Reconstruction","date":"2020-07-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pifuhd-multi-level-pixel-aligned-implicit","title":"PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization","date":"2020-04-01","rows_on_this_dataset":1,"code_links":3,"syntology":null},{"paper":"/paper/pifu-pixel-aligned-implicit-function-for-high","title":"PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization","date":"2019-05-13","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":1,"samples_harvested":3,"samples_ran":3,"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."}