# RIT-Eyes: Rendering of near-eye images for eye-tracking applications

arXiv: https://arxiv.org/abs/2006.03642 · HTML: https://mlsanity.com/paper/2006.03642/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jun 2020.

## Built on by (most cited first, 10 of 10)

- [Zero-Shot Segmentation of Eye Features Using the Segment Anything Model (SAM)](https://mlsanity.com/paper/2311.08077/) (2023)
- [EllSeg-Gen, towards Domain Generalization for head-mounted eyetracking](https://mlsanity.com/paper/2205.01947/) (2022)
- [Few-shot Geometry-Aware Keypoint Localization](https://mlsanity.com/paper/2303.17216/) (2023)
- [Deep Domain Adaptation: A Sim2Real Neural Approach for Improving Eye-Tracking Systems](https://mlsanity.com/paper/2403.15947/) (2024)
- [LEyes: A Lightweight Framework for Deep Learning-Based Eye Tracking using Synthetic Eye Images](https://mlsanity.com/paper/2309.06129/) (2023)
- [CondSeg: Ellipse Estimation of Pupil and Iris via Conditioned Segmentation](https://mlsanity.com/paper/2408.17231/) (2024)
- [Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images](https://mlsanity.com/paper/2410.08926/) (2024)
- [Digitally Prototype Your Eye Tracker: Simulating Hardware Performance using 3D Synthetic Data](https://mlsanity.com/paper/2503.16742/) (2025)
- [Using Deep Learning to Increase Eye-Tracking Robustness, Accuracy, and Precision in Virtual Reality](https://mlsanity.com/paper/2403.19768/) (2024)
- [GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning](https://mlsanity.com/paper/2609.17814/) (2026)

## Abstract (arXiv)

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 a synthetic eye image generation platform that improves upon previous work by adding features such as an active deformable iris, an aspherical cornea, retinal retro-reflection, gaze-coordinated eye-lid deformations, and blinks. To demonstrate the utility of our platform, we render images reflecting the represented gaze distributions inherent in two publicly available datasets, NVGaze and OpenEDS. We also report on the performance of two semantic segmentation architectures (SegNet and RITnet) trained on rendered images and tested on the original datasets.