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GazeFlow framework predicts egocentric gaze using conditional flow matching

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced GazeFlow, a new framework designed to predict egocentric gaze trajectories by modeling gaze as a joint distribution of temporal positions. This model utilizes conditional flow matching (CFM) to learn a velocity field that transforms a Gaussian noise sample into a plausible gaze trajectory. GazeFlow conditions this velocity field on both visual features extracted from video and top-down task information, achieving state-of-the-art performance on standard datasets and demonstrating improved alignment with human gaze dynamics. AI

IMPACT This research advances egocentric gaze prediction, potentially improving applications that rely on understanding human visual attention.

RANK_REASON The cluster contains a research paper detailing a new framework for gaze prediction. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

GazeFlow framework predicts egocentric gaze using conditional flow matching

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26 / 100
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The cluster contains a research paper detailing a new framework for gaze prediction. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Sheng Zhao, Weikai Lin, Yuhao Zhu ·

    GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction

    arXiv:2609.38519v1 Announce Type: new Abstract: Egocentric gaze prediction enables many downstream applications but remains challenging, as human gaze is inherently stochastic. This stochasticity is constrained by structured temporal dynamics alternating between fixations and sac…

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