{"url":"/dataset/nvgaze","name":"NVGaze","full_name":"NVGaze: An Anatomically-Informed Dataset for Low-Latency, Near-Eye Gaze Estimation","description_markdown":"Quality, diversity, and size of training dataset are critical factors for learning-based gaze estimators. We create two datasets satisfying these criteria for near-eye gaze estimation under infrared illumination: a synthetic dataset using anatomically-informed eye and face models with variations in face shape, gaze direction, pupil and iris, skin tone, and external conditions (two million images at 1280x960), and a real-world dataset collected with 35 subjects (2.5 million images at 640x480). Using our datasets, we train a neural network for gaze estimation, achieving 2.06 (+/- 0.44) degrees of accuracy across a wide 30 x 40 degrees field of view on real subjects excluded from training and 0.5 degrees best-case accuracy (across the same field of view) when explicitly trained for one real subject. We also train a variant of our network to perform pupil estimation, showing higher robustness than previous methods. Our network requires fewer convolutional layers than previous networks, achieving sub-millisecond latency.","description_withheld":null,"homepage":"https://research.nvidia.com/publication/2019-05_NVGaze%3A-An-Anatomically-Informed","introduced_date":"2019-05-04","introduced_date_note":null,"introduced_by":null,"license":{"name":"Custom","url":"https://research.nvidia.com/publication/2019-05_NVGaze%3A-An-Anatomically-Informed"},"modalities":[],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["NVGaze"],"data_loaders":[],"num_papers_in_archive":2,"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."}