English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Eye gaze estimation and simultaneous semantic understanding of a user througheye images is a crucial component in Virtual and Mixed Reality; enabling energyefficient rendering, multi-focal displays and effective interaction with 3Dcontent. In head-mounted VR/MR devices the eyes are imaged off-axis to avoidblocking the user's gaze, this view-point makes drawing eye related inferencesvery challenging. In this work, we present EyeNet, the first single deep neuralnetwork which solves multiple heterogeneous tasks related to eye gazeestimation and semantic user understanding for an off-axis camera setting. Thetasks include eye segmentation, blink detection, emotive expressionclassification, IR LED glints detection, pupil and cornea center estimation. Totrain EyeNet end-to-end we employ both hand labelled supervision and modelbased supervision. We benchmark all tasks on MagicEyes, a large and new datasetof 587 subjects with varying morphology, gender, skin-color, make-up andimaging conditions.

EyeNet: A Multi-Task Network for Off-Axis Eye Gaze Estimation and User Understanding