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 Tools annual

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 Tools monthly

Global: Zendy Plus monthly

Presents the access of premium content as premium feature

Premium Content

Global: Zendy Plus annual

Global: Zendy Free Plan

The reconstruction of dense 3D models of face geometry and appearance from asingle image is highly challenging and ill-posed. To constrain the problem,many approaches rely on strong priors, such as parametric face models learnedfrom limited 3D scan data. However, prior models restrict generalization of thetrue diversity in facial geometry, skin reflectance and illumination. Toalleviate this problem, we present the first approach that jointly learns 1) aregressor for face shape, expression, reflectance and illumination on the basisof 2) a concurrently learned parametric face model. Our multi-level face modelcombines the advantage of 3D Morphable Models for regularization with theout-of-space generalization of a learned corrective space. We train end-to-endon in-the-wild images without dense annotations by fusing a convolutionalencoder with a differentiable expert-designed renderer and a self-supervisedtraining loss, both defined at multiple detail levels. Our approach comparesfavorably to the state-of-the-art in terms of reconstruction quality, bettergeneralizes to real world faces, and runs at over 250 Hz.

Self-supervised Multi-level Face Model Learning for Monocular Reconstruction at over 250 Hz