{"url":"/dataset/riteyes","name":"RITEyes","full_name":null,"description_markdown":"Deep neural networks for video based eye tracking have demonstrated resilience to noisy environments, stray reflections and low resolution. However, to train these networks, a large number of manually annotated images are required. To alleviate the cumbersome process of manual labeling, computer graphics rendering is employed to automatically generate a large corpus of annotated eye images under various conditions. In this work, we introduce RIT-Eyes, a novel synthetic eye image generation platform which improves upon previous work by adding features such as retinal retro-reflection, realistic blinks, an active deformable iris and an aspherical cornea. We add various external influences which potentially degrade eye tracking such as corrective eye-wear with varying refractive indices. To demonstrate the utility of RIT-Eyes, we generate and publicly share a large dataset of images with a variety of eye poses and viewing conditions.","description_withheld":null,"homepage":"https://cs.rit.edu/~cgaplab/RIT-Eyes/","introduced_date":"2020-06-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/rit-eyes-rendering-of-near-eye-images-for-eye","title":"RIT-Eyes: Rendering of near-eye images for eye-tracking applications","first_author":"Nitinraj Nair","url":null},"license":{"name":"MIT","url":null},"modalities":[],"tasks":[],"languages":[],"variants":["RITEyes"],"data_loaders":[],"num_papers_in_archive":4,"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."}