  We present a large-scale facial UV-texture dataset that contains over 50,000
high-quality texture UV-maps with even illuminations, neutral expressions, and
cleaned facial regions, which are desired characteristics for rendering
realistic 3D face models under different lighting conditions. The dataset is
derived from a large-scale face image dataset namely FFHQ, with the help of our
fully automatic and robust UV-texture production pipeline. Our pipeline
utilizes the recent advances in StyleGAN-based facial image editing approaches
to generate multi-view normalized face images from single-image inputs. An
elaborated UV-texture extraction, correction, and completion procedure is then
applied to produce high-quality UV-maps from the normalized face images.
Compared with existing UV-texture datasets, our dataset has more diverse and
higher-quality texture maps. We further train a GAN-based texture decoder as
the nonlinear texture basis for parametric fitting based 3D face
reconstruction. Experiments show that our method improves the reconstruction
accuracy over state-of-the-art approaches, and more importantly, produces
high-quality texture maps that are ready for realistic renderings. The dataset,
code, and pre-trained texture decoder are publicly available at
https://github.com/csbhr/FFHQ-UV.
