# A High-Level Description and Performance Evaluation of Pupil Invisible

arXiv: https://arxiv.org/abs/2009.00508 · HTML: https://mlsanity.com/paper/2009.00508/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [Voila-A: Aligning Vision-Language Models with User's Gaze Attention](https://mlsanity.com/paper/2401.09454/) (2023)
- [A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding](https://mlsanity.com/paper/2307.01470/) (2023)
- [G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios](https://mlsanity.com/paper/2405.07652/) (2024)
- [One step closer to EEG based eye tracking](https://mlsanity.com/paper/2303.06039/) (2023)
- [Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction](https://mlsanity.com/paper/2312.08558/) (2023)
- [Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation](https://mlsanity.com/paper/2409.09135/) (2024)
- [NMF-Based Analysis of Mobile Eye-Tracking Data](https://mlsanity.com/paper/2404.03417/) (2024)
- [Resolving Ambiguity in Gaze-Facilitated Visual Assistant Interaction Paradigm](https://mlsanity.com/paper/2509.21980/) (2025)
- [AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination](https://mlsanity.com/paper/2606.03774/) (2026)
- [TransGaze-Object: Transformer Based Driver Gaze Object Prediction Framework in Real Driving](https://mlsanity.com/paper/2609.10139/) (2026)
- [From Laboratory to Road: Evaluating Wearable Gaze Accuracy for Driving](https://mlsanity.com/paper/2610.07783/) (2026)
- [PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling](https://mlsanity.com/paper/2610.10370/) (2026)

## Abstract (arXiv)

Head-mounted eye trackers promise convenient access to reliable gaze data in unconstrained environments. Due to several limitations, however, often they can only partially deliver on this promise. Among those are the following: (i) the necessity of performing a device setup and calibration prior to every use of the eye tracker, (ii) a lack of robustness of gaze-estimation results against perturbations, such as outdoor lighting conditions and unavoidable slippage of the eye tracker on the head of the subject, and (iii) behavioral distortion resulting from social awkwardness, due to the unnatural appearance of current head-mounted eye trackers. Recently, Pupil Labs released Pupil Invisible glasses, a head-mounted eye tracker engineered to tackle these limitations. Here, we present an extensive evaluation of its gaze-estimation capabilities. To this end, we designed a data-collection protocol and evaluation scheme geared towards providing a faithful portrayal of the real-world usage of Pupil Invisible glasses. In particular, we develop a geometric framework for gauging gaze-estimation accuracy that goes beyond reporting mean angular accuracy. We demonstrate that Pupil Invisible glasses, without the need of a calibration, provide gaze estimates which are robust to perturbations, including outdoor lighting conditions and slippage of the headset.