{"url":"/dataset/uli-ri","name":"ULI-RI","full_name":"Unreal Labeled Images for Person Re-ID","description_markdown":"The ULI-RI dataset is generated using the Unreal Engine 4 to simulate various outdoor environments with 115 high-quality 3D human models. \r\nFor each person identity, we controlled and quantitatively labeled the illumination intensity, view point (model z-rotation angle), and background to create 512 images.\r\nThere are total 115 x 512 = 58880 images in the ULI-RI dataset.","description_withheld":null,"homepage":"https://lorenz.ecn.purdue.edu/~guo498/ssiai2024","introduced_date":"2023-11-13","introduced_date_note":null,"introduced_by":{"paper":"/paper/illumination-variation-correction-using-image","title":"Illumination Variation Correction Using Image Synthesis For Unsupervised Domain Adaptive Person Re-Identification","first_author":"Jiaqi Guo","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Person Re-Identification","url":"/task/person-re-identification","datasets_with_task":"/datasets/task/person-re-identification"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["ULI-RI"],"data_loaders":[],"num_papers_in_archive":1,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[],"papers_with_a_benchmark_row":[],"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."}