# OpenEDS2020: Open Eyes Dataset

arXiv: https://arxiv.org/abs/2005.03876 · HTML: https://mlsanity.com/paper/2005.03876/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 May 2020.

## Built on by (most cited first, 20 of 20)

- [Automatic Gaze Analysis: A Survey of Deep Learning based Approaches](https://mlsanity.com/paper/2108.05479/) (2021)
- [Segmentation in Style: Unsupervised Semantic Image Segmentation with Stylegan and CLIP](https://mlsanity.com/paper/2107.12518/) (2021)
- [MTGLS: Multi-Task Gaze Estimation with Limited Supervision](https://mlsanity.com/paper/2110.12100/) (2021)
- [Semi-Supervised Learning for Eye Image Segmentation](https://mlsanity.com/paper/2103.09369/) (2021)
- [FlatTrack: Eye-tracking with ultra-thin lensless cameras](https://mlsanity.com/paper/2501.15450/) (2025)
- [CondSeg: Ellipse Estimation of Pupil and Iris via Conditioned Segmentation](https://mlsanity.com/paper/2408.17231/) (2024)
- [Towards Unsupervised Eye-Region Segmentation for Eye Tracking](https://mlsanity.com/paper/2410.06131/) (2024)
- [Zero-Shot Pupil Segmentation with SAM 2: A Case Study of Over 14 Million Images](https://mlsanity.com/paper/2410.08926/) (2024)
- [VRBiom: A New Periocular Dataset for Biometric Applications of HMD](https://mlsanity.com/paper/2407.02150/) (2024)
- [Energy-Efficient & Real-Time Computer Vision with Intelligent Skipping via Reconfigurable CMOS Image Sensors](https://mlsanity.com/paper/2409.17341/) (2024)
- [Iris Style Transfer: Enhancing Iris Recognition with Style Features and Privacy Preservation through Neural Style Transfer](https://mlsanity.com/paper/2503.04707/) (2025)
- [Trade-offs in Privacy-Preserving Eye Tracking through Iris Obfuscation: A Benchmarking Study](https://mlsanity.com/paper/2504.10267/) (2025)
- [CycleSL: Server-Client Cyclical Update Driven Scalable Split Learning](https://mlsanity.com/paper/2511.18611/) (2025)
- [GazeShift: Unsupervised Gaze Estimation and Dataset for VR](https://mlsanity.com/paper/2603.07832/) (2026)
- [Rapidly deploying on-device eye tracking by distilling visual foundation models](https://mlsanity.com/paper/2604.02509/) (2026)
- ['Labelling the Gaps': A Weakly Supervised Automatic Eye Gaze Estimation](https://mlsanity.com/paper/2208.01840/) (2022)
- [A3FR: Agile 3D Gaussian Splatting with Incremental Gaze Tracked Foveated Rendering in Virtual Reality](https://mlsanity.com/paper/2507.04147/) (2025)
- [Night Eyes: A Reproducible Framework for Constellation-Based Corneal Reflection Matching](https://mlsanity.com/paper/2604.01909/) (2026)
- [PicoEyes: Unified Gaze Estimation Framework for Mixed Reality with a Large-Scale Multi-View Dataset](https://mlsanity.com/paper/2605.07188/) (2026)
- [Hardware-Aware Learned Representation Compression for Distributed In-Sensor Vision](https://mlsanity.com/paper/2609.13947/) (2026)

## Abstract (arXiv)

We present the second edition of OpenEDS dataset, OpenEDS2020, a novel dataset of eye-image sequences captured at a frame rate of 100 Hz under controlled illumination, using a virtual-reality head-mounted display mounted with two synchronized eye-facing cameras. The dataset, which is anonymized to remove any personally identifiable information on participants, consists of 80 participants of varied appearance performing several gaze-elicited tasks, and is divided in two subsets: 1) Gaze Prediction Dataset, with up to 66,560 sequences containing 550,400 eye-images and respective gaze vectors, created to foster research in spatio-temporal gaze estimation and prediction approaches; and 2) Eye Segmentation Dataset, consisting of 200 sequences sampled at 5 Hz, with up to 29,500 images, of which 5% contain a semantic segmentation label, devised to encourage the use of temporal information to propagate labels to contiguous frames. Baseline experiments have been evaluated on OpenEDS2020, one for each task, with average angular error of 5.37 degrees when performing gaze prediction on 1 to 5 frames into the future, and a mean intersection over union score of 84.1% for semantic segmentation. As its predecessor, OpenEDS dataset, we anticipate that this new dataset will continue creating opportunities to researchers in eye tracking, machine learning and computer vision communities, to advance the state of the art for virtual reality applications. The dataset is available for download upon request at http://research.fb.com/programs/openeds-2020-challenge/.