arXiv Computer Vision
GazeDiT: Gaze-Accurate Diffusion Image Generation for Eye Tracking via Spatial Conditioning
Read the original on arXiv Computer Vision →The Flow has not summarised this story yet — read it at arXiv Computer Vision.
Related stories
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
Sep 1
Drift Calibration in Geometric Eye Tracking Systems
arXiv:2608.29739v1 Announce Type: new Abstract: Geometric eye trackers can provide the spatial accuracy required for gaze-based interaction and multimodal studies, but their measurements remain sensi...
arXiv Computer Vision
Sep 7
EyeMakeYou: Identity-, Task-, and Subjective-State-Conditioned Diffusion for High-Frequency Gaze Synthesis
EyeMakeYou is a multi‑conditional denoising diffusion model that synthesizes high‑frequency, subject‑specific gaze velocity sequences. It conditions on identity, task, and self‑reported subjective states (difficulty, mental tiredness, eye tiredness) to generate realistic 5‑second, 1000‑Hz bivariate gaze data from a reference trajectory. Experiments on the GazeBase dataset show that EyeMakeYou outperforms existing generative methods in spatial accuracy and real‑synthetic similarity while preserving task‑dependent associations with subjective reports.
arXiv AI
Jul 15
Look, Focus, Act: Efficient and Robust Robot Learning via Human Gaze and Foveated Vision Transformers
arXiv:2507. 15833v3 Announce Type: replace-cross Abstract: Human vision is a highly active process driven by gaze, which directs attention to task-relevant regions through foveation, dramatically reducing visual processing.
Hugging Face Trending Papers
Aug 11
Gaze Target Estimation Anywhere with Concepts
Estimating human gaze targets from images in-the-wild is an important and formidable task. Existing approaches primarily employ brittle, multi-stage pipelines that require explicit inputs, like head bounding boxes and human pose, in order to identify the subject of gaze analysis.
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.