arXiv Computer Vision
Motion-Based Tokenization for Cross-Dataset Egocentric Gaze Modeling
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arXiv Computer Vision
Oct 2
EyeTAG: Eye Trajectory-Aware Gaze Estimation
arXiv:2610.00922v1 Announce Type: new Abstract: Gaze estimation under natural head-eye motion underpins applications from driver monitoring to human-computer interaction. Single-frame methods predict...
arXiv Computer Vision
Oct 21
Gaze2HOI: Gaze-Aware Intention Disambiguation for Egocentric Hand-Object Interaction Forecasting
arXiv:2610.27877v1 Announce Type: new Abstract: Anticipating how users will interact with their surroundings is essential for proactive systems that provide timely assistance in AR, VR, and embodied...
arXiv Computer Vision
Sep 14
Context-Aware Causal Gaze Forecasting for Human-Vehicle Interaction During In-Cabin Tracking Dropouts
The paper introduces the Causal Context-Gated Forecaster (CCGF) for predicting a driver's gaze during dashboard-mounted tracker dropouts. CCGF uses a 60‑frame history of gaze and head pose combined with DINOv3 scene features, and a learned reliability gate adjusts the influence of these inputs as the dropout progresses. Experiments on 2,047 naturalistic driving events show that live scene updates reduce median error by 33% compared to history‑only forecasting, while frozen scene input yields higher error, demonstrating the value of real‑time scene information.
arXiv AI
Sep 10
Diffusion models for eye-gaze trajectory generation using position and velocity representations
arXiv:2609.05522v1 Announce Type: cross Abstract: Eye-tracking data are expensive to collect, requiring specialized hardware and controlled laboratory conditions, and difficult to share because of pr...
arXiv Computer Vision
Oct 1
GazeFlow: From Human Gaze Behavior to Generative Egocentric Gaze Prediction
GazeFlow is a new framework for egocentric gaze prediction that models gaze as a joint distribution of temporal positions conditioned on both top‑down task cues and bottom‑up visual saliency. It employs conditional flow matching to iteratively transform Gaussian noise into realistic gaze trajectories, using a velocity field informed by video‑encoded visual features and global task queries. On standard benchmarks, GazeFlow outperforms existing methods on per‑frame metrics and produces trajectories that better reflect human gaze dynamics.
arXiv AI
Sep 4
GazeFS: Target-Centered Gaze-Trajectory Forecasting and Stabilization from Gaze-Head History
GazeFS is a model that predicts and stabilizes target‑centered gaze trajectories using a variable‑length gaze‑head history, without requiring target information during inference. It maps this history to the next target‑center direction and a short‑horizon Search/Focus estimate, improving focus target centering and reducing residual gaze error. Across 7,960 acquisition episodes from 30 participants, GazeFS reduces Focus episode bias, dispersion, and P90 target error by 0.182°, 0.257°, and 0.400°, respectively, while maintaining high phase‑balanced accuracy and AUPRC.