Search Results for author:
Found 4 papers, 2 papers with code
Weakly-Supervised Physically Unconstrained Gaze Estimation
A major challenge for physically unconstrained gaze estimation is acquiring training data with 3D gaze annotations for in-the-wild and outdoor scenarios.
Ranked #3 on
Gaze Estimation
on Gaze360
RIT-Eyes: Rendering of near-eye images for eye-tracking applications
Deep neural networks for video-based eye tracking have demonstrated resilience to noisy environments, stray reflections, and low resolution.
RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues such as person-dependent accuracy, lack of robustness, and an inability to be run in real-time.
Ranked #1 on
Semantic Segmentation
on OpenEDS
Gaze-in-wild: A dataset for studying eye and head coordination in everyday activities
Our approach was to collect a novel, naturalistic, and multimodal dataset of eye+head movements when subjects performed everyday tasks while wearing a mobile eye tracker equipped with an inertial measurement unit and a 3D stereo camera.
