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

We present a large-scale facial UV-texture dataset that contains over 50,000high-quality texture UV-maps with even illuminations, neutral expressions, andcleaned facial regions, which are desired characteristics for renderingrealistic 3D face models under different lighting conditions. The dataset isderived from a large-scale face image dataset namely FFHQ, with the help of ourfully automatic and robust UV-texture production pipeline. Our pipelineutilizes the recent advances in StyleGAN-based facial image editing approachesto generate multi-view normalized face images from single-image inputs. Anelaborated UV-texture extraction, correction, and completion procedure is thenapplied to produce high-quality UV-maps from the normalized face images.Compared with existing UV-texture datasets, our dataset has more diverse andhigher-quality texture maps. We further train a GAN-based texture decoder asthe nonlinear texture basis for parametric fitting based 3D facereconstruction. Experiments show that our method improves the reconstructionaccuracy over state-of-the-art approaches, and more importantly, produceshigh-quality texture maps that are ready for realistic renderings. The dataset,code, and pre-trained texture decoder are publicly available athttps://github.com/csbhr/FFHQ-UV.

FFHQ-UV: Normalized Facial UV-Texture Dataset for 3D Face Reconstruction