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
Sep 17

GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning

GazeDiT is a diffusion model that generates synthetic eye images conditioned on a precise 4‑dimensional gaze vector by using a spatial condition derived from pupil and iris geometry. During training, a frozen SegFormer extracts this geometry from real images, while inference employs a physical eye renderer to produce gaze‑consistent geometries without needing a source image. The model achieves lower tail gaze‑label error than other diffusion baselines and improves downstream eye‑tracking accuracy, reducing error on challenging cases from 3.05° to 2.80°.

By Dongze Wu, David Colmenares, Fengting Yang, Jogendra Nath Kundu, Yao Xie, Ali Behrooz, Conny Lu