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ARXIV:2604.02509 · ON-DEVICE EYE TRACKING · SUBMITTED 06 APR · 20:16 UTC · FRESHNESS UNKNOWN
ARXIV:2604.02509ON-DEVICE EYE TRACKINGSUBMITTED 06 APR · 20:16 UTCFRESHNESS UNKNOWNCheng Jiang · Jogendra Kundu · David Colmenares · Fengting Yang · Joseph Robinson · Yatong An · +1 at arXiv
A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data.
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Pain A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data.
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A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations…
Eye tracking (ET) plays a critical role in augmented and virtual reality applications. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations (e.g., camera placement, camera pose, and…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Visual foundation models (VFMs) are a promising direction for rapid training and deployment, and they excel on natural-image benchmarks; yet we find that off-the-shelf…
On-Device Eye Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
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A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data.
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10.48550/arXiv.2604.02509A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data.
Abstract
Eye tracking (ET) plays a critical role in augmented and virtual reality applications. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations (e.g., camera placement, camera pose, and illumination) often change across device generations. Visual foundation models (VFMs) are a promising direction for rapid training and deployment, and they excel on natural-image benchmarks; yet we find that off-the-shelf VFMs still struggle to achieve high accuracy on specialized near-eye infrared imagery. To address this gap, we introduce DistillGaze, a framework that distills a foundation model by leveraging labeled synthetic data and unlabeled real data for rapid and high-performance on-device gaze estimation. DistillGaze proceeds in two stages. First, we adapt a VFM into a domain-specialized teacher using self-supervised learning on labeled synthetic and unlabeled real images. Synthetic data provides scalable, high-quality gaze supervision, while unlabeled real data helps bridge the synthetic-to-real domain gap. Second, we train an on-device student using both teacher guidance and self-training. Evaluated on a large-scale, crowd-sourced dataset spanning over 2,000 participants, DistillGaze reduces median gaze error by 58.62% relative to synthetic-only baselines while maintaining a lightweight 256K-parameter model suitable for real-time on-device deployment. Overall, DistillGaze provides an efficient pathway for training and deploying ET models that adapt to hardware changes, and offers a recipe for combining synthetic supervision with unlabeled real data in on-device regression tasks.
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PROBLEM
A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configura...
METHOD
Eye tracking (ET) plays a critical role in augmented and virtual reality applications. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations (e.g., camera placement, camera pose, and illumination)...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Visual foundation models (VFMs) are a promising direction for rapid training and deployment, and they excel on natural-image benchmarks; yet we find that off-the-shelf VFMs still struggle to achieve high...
WHY NOW
On-Device Eye Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed public claims while anchored extraction refreshes.
A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations (e.g., camera placement, camera pose, and illumination) often change across device generations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Eye tracking (ET) plays a critical role in augmented and virtual reality applications. However, rapidly deploying high-accuracy, on-device gaze estimation for new products remains challenging because hardware configurations (e.g., camera placement, camera pose, and illumination) often change across device generations.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. Visual foundation models (VFMs) are a promising direction for rapid training and deployment, and they excel on natural-image benchmarks; yet we find that off-the-shelf VFMs still struggle to achieve high accuracy on specialized near-eye infrared imagery. Code availability is flagged in the production record; the public repository link still needs proof alignment.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
On-Device Eye Tracking moved forward this cycle; last verified April 2026. Public score 7.0/10. Production flags indicate code availability.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework for rapidly deploying high-accuracy, on-device eye tracking by distilling visual foundation models using synthetic and real-world data.
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