A High-Level Description and Performance Evaluation of Pupil Invisible
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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.
Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.
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Citing papers per year: 2023: 4; 2024: 3; 2025: 1; 2026: 3.
11 papers.
- 2023Voila-A: Aligning Vision-Language Models with User's Gaze Attentioncited by 28
- 2023A Review of Driver Gaze Estimation and Application in Gaze Behavior Understandingcited by 12
- 2024G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarioscited by 8
- 2023One step closer to EEG based eye trackingcited by 3
- 2024Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversationcited by 3
- 2023Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Predictioncited by 2
- 2024NMF-Based Analysis of Mobile Eye-Tracking Datacited by 0
- 2025Resolving Ambiguity in Gaze-Facilitated Visual Assistant Interaction Paradigmcited by 0
- 2026AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illuminationcited by 0
- 2026TransGaze-Object: Transformer Based Driver Gaze Object Prediction Framework in Real Drivingcited by 0
- 2026PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modelingcited by 0
- 2026PalmSpace: Towards a Versatile On-Palm Interaction Space through Unified Touch Modeling7 Oct 2026
- 2026TransGaze-Object: Transformer Based Driver Gaze Object Prediction Framework in Real Driving9 Sep 2026
- 2026AmbientEye: A Dataset for Pupil Segmentation under Natural Ambient Infrared Illumination2 Jun 2026
- 2025Resolving Ambiguity in Gaze-Facilitated Visual Assistant Interaction Paradigm26 Sep 2025
- 2024Multimodal Fusion with LLMs for Engagement Prediction in Natural Conversation13 Sep 2024
- 2024G-VOILA: Gaze-Facilitated Information Querying in Daily Scenarios13 May 2024
- 2024NMF-Based Analysis of Mobile Eye-Tracking Data4 Apr 2024
- 2023Voila-A: Aligning Vision-Language Models with User's Gaze Attention22 Dec 2023
- 2023Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction13 Dec 2023
- 2023A Review of Driver Gaze Estimation and Application in Gaze Behavior Understanding4 Jul 2023
- 2023One step closer to EEG based eye tracking3 Mar 2023