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Related Experiment Video

Updated: Aug 30, 2025

Using Eye Movements Recorded in the Visual World Paradigm to Explore the Online Processing of Spoken Language
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Temporal RIT-Eyes: From Real Infrared Eye-Images to Synthetic Sequences of Gaze Behavior.

Aayush K Chaudhary, Nitinraj Nair, Reynold J Bailey

    IEEE Transactions on Visualization and Computer Graphics
    |August 31, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Temporal RIT-Eyes generates synthetic eye images with realistic gaze dynamics for training segmentation models. This novel pipeline captures eyelid motion, improving eye image analysis beyond static datasets.

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    Area of Science:

    • Computer Vision
    • Biomedical Imaging
    • Machine Learning

    Background:

    • Current eye image segmentation methods fail to incorporate eye motion due to static training datasets.
    • Lack of dynamic data limits the performance of models in real-world eye-tracking applications.

    Purpose of the Study:

    • To introduce Temporal RIT-Eyes, a Blender pipeline for generating synthetic eye imagery with natural gaze dynamics.
    • To create temporally consistent, ground-truth annotated eye image datasets for training advanced segmentation networks.
    • To enable the modeling of gaze-dependent eyelid pose and blink behavior.

    Main Methods:

    • Developed a novel method for extracting 3D eyelid pose from raw eye images.
    • Utilized real eye videos to render synthetic imagery with controlled variations in appearance, geometry, and environment.
    • Created two open-source datasets: sGiW (Gaze in Wild dynamics) and sOpenEDS2 (OpenEDS-2019 approximation).

    Main Results:

    • Generated synthetic eye image sequences with realistic gaze dynamics and accompanying ground-truth segmentation maps.
    • Demonstrated the pipeline's ability to render gaze-dependent eyelid pose and blink behavior.
    • Qualitative analysis confirmed the high quality of rendered data with significant overlap in latent-space representations.

    Conclusions:

    • Temporal RIT-Eyes effectively addresses the limitations of static datasets for eye image segmentation.
    • The generated synthetic datasets facilitate the development of more robust eye-tracking and segmentation models.
    • This approach enhances the potential for accurate analysis of eye motion and behavior in diverse conditions.