arXiv:2607. 05473v1 Announce Type: cross Abstract: According to commonly consented theories, the minimum hardware requirement for gaze tracker is one camera and two light sources to realize gaze estimation with free head movements.
According to commonly consented theories, the minimum hardware requirement for gaze tracker is one camera and two light sources to realize gaze estimation with free head movements. However, in some scenarios such as eye tracking on mobile devices, it is preferable to use less components, especially light sources.
arXiv:2609.09610v1 Announce Type: new Abstract: This paper presents a marker-free eye-gaze estimation approach using a single 2D camera, such as an integrated laptop webcam. The gaze-related features...
arXiv:2610.07783v1 Announce Type: new Abstract: Bird's-eye-view (BEV) representations have become a widely used interface between perception and planning in autonomous driving, but they encode what i...
This paper evaluates current gaze estimation methods in a human‑robot interaction setting using the NICO robotic platform. A new annotated dataset was collected, and four state‑of‑the‑art models were tested, showing angular errors comparable to general benchmarks. In the shared workspace, the best median error was 14.57 cm, highlighting practical limitations and prompting recommendations for integrating gaze estimation into HRI systems.
arXiv:2502.10570v3 Announce Type: replace-cross Abstract: Mobile gaze tracking on handheld devices commonly relies on \emph{appearance-based 2D gaze estimation} from the device's \emph{front-facing R...
arXiv:2608. 15614v1 Announce Type: cross Abstract: The use of multimodal LLMs (MLLMs) for egocentric video understanding with wearable devices is constrained by the token budget.
This paper investigates how key factors affect the performance of gaze-based authentication systems. Using a large in-house dataset of 8,849 subjects recorded with Meta Quest Pro–equivalent hardware and a 72 Hz video oculography pipeline, the study evaluates the impact of eye‑tracking signal quality, calibration settings, and simple filtering on neural‑network‑based authentication. The authors present performance results and an analysis of these influences.
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:2609.09188v1 Announce Type: new Abstract: Lensless near-eye sensing is often described as privacy-friendly because its coded measurements are visually unintelligible. Yet visual unintelligibili...
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°.
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...
EgoHRV is a method that estimates heart rate variability (HRV) and heart rate (HR) from the gaze cameras in egocentric headsets. It uses a 3D backbone and a low–high decomposition module to extract the blood volume pulse signal from gaze video, and aligns frequency‑domain representations of contact‑based and camera‑derived signals through cross‑domain pretraining. The approach achieves state‑of‑the‑art accuracy for HR and HRV estimation and, when integrated into EgoExo4D’s proficiency estimator, improves accuracy by 17.8%.