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.

By Yaozheng Xia, Zaiping Zhu, Bo Pang, Minghao Xie, Hui Li, Shaorong Wang, Sheng Li
arXiv Computer Vision
4d ago

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.

By Shabnam Shabani, Ghazal Farhani