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.
By Matej Palider, Omar Eldardeer, Viktor Kocur
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...
By William Engel, Fabian Flohr
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...
By David Hurtubise-Martin, Feriel Fass, Djemel Ziou, Marie-Flavie Auclair-Fortier
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.
By Tongbing Huang, Yang Fu, Yunfei Wang, Zhaocan Wang
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: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...
By Yaxiong Lei, Yuheng Wang, Fergus Buchanan, Mingyue Zhao, Yusuke Sugano, Shijing He, Mohamed Khamis, Juan Ye
The paper investigates how different gaze data representations affect privacy and utility in extended reality (XR) systems. Three representations—raw gaze, spatial attention heatmaps, and engineered eye‑movement features—are compared using the HoloAssist dataset, evaluating action recognition accuracy and closed‑set user re‑identification. Engineered features preserve about 85% of action‑recognition performance while reducing re‑identification risk by roughly an order of magnitude, yet still leave some identity leakage, indicating that abstraction alone does not guarantee privacy.
By Cory Ilo, Brendan-David John, Doug A. Bowman
arXiv:2608. 08947v1 Announce Type: cross Abstract: Current hazard detection systems in autonomous driving may develop mesa objectives, learned internal goals that achieve high training performance through spurious correlations rather than genuine hazard recognition.
By Lennox Anderson, Ahmed Boutar, Jonah Mulcrone, Tal Erez
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...
By Rahul Vimalkanth, Kaushik Mitra
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%.
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
arXiv:2610.08663v1 Announce Type: new
Abstract: Video gaze prediction is led by gaze-trained models, yet gaze-free priors carry signal those models have not absorbed, if one knows when to trust them....
By Lichen Zhu, Yueqian Lin, Yiheng Wang, Hai "Helen" Li, Yiran Chen