DiTGaze is a calibration‑efficient augmentation system for few‑shot gaze estimation that uses a two‑stream 3D Gaussian Splatting backbone and a geometry‑conditioned latent Diffusion Transformer. It bridges rasterized Gaussian features to latent tokens via a learnable projection and frozen VAE, and incorporates timestep, identity, and expression conditioning through Adaptive Layer Normalization. The method densifies gaze supervision with intermediate yaw‑pitch sampling and applies an orthogonality constraint to reduce interference among gaze, pose, and expression, achieving a 4.1 % reduction in generator‑level gaze error on ETH‑XGaze and outperforming baselines across all calibration budgets up to nine real images.
By Abiram Panchalingam, Indu P. Bodala, Stuart E. Middleton
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...
By Laxman Basnet, Alexander Szorkovszky, Pedro G. Lind, Anis Yazidi, Shailendra Bhandari
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...
By Jungmin Lee, Niamat Ullah, Yoseob Han
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.
By Sheng Zhao, Weikai Lin, Yuhao Zhu
arXiv:2211.11062v2 Announce Type: replace
Abstract: Gaze following aims to predict where a person is looking in a scene, by predicting the target location, or indicating that the target is located ou...
By Qiaomu Miao, Minh Hoai, Dimitris Samaras
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...
By Jiaqi Liu, Zixuan Wang, Yuhong Zhang, Dingkang Liang, Jane Hanqi Li, Tzyy-Ping Jung, Gert Cauwenberghs
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.
By Kamrul Hasan, Mehedi Hasan Raju, Oleg V. Komogortsev
arXiv:2603.26945v2 Announce Type: replace
Abstract: Appearance-based gaze estimation (AGE) has achieved remarkable performance in constrained settings, yet we reveal a significant generalization gap...
By Zhenhao Li, Zheng Liu, Seunghyun Lee, Amin Fadaeinejad, Yuanhao Yu
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.
By Ian Chuang, Jinyu Zou, Andrew Lee, Dechen Gao, Iman Soltani
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.
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
FoAViS is a foveated active vision system that samples visual input using a log‑polar foveation and stores information in a 2D spatial memory. The system’s fixation policy is trained concurrently with tasks such as classification and semantic segmentation, allowing the same memory to be read out globally or densely without architectural changes. Learned fixation policies outperform non‑learned heuristics, improving task performance and producing scanpaths that resemble human gaze behavior.
By Valentin Hassler, Jonathan Henrich, Yoha\"i-Eliel Berreby, Haoze Zhu, Alexander Ecker