  In this paper, we present a large-scale detailed 3D face dataset, FaceScape,
and the corresponding benchmark to evaluate single-view facial 3D
reconstruction. By training on FaceScape data, a novel algorithm is proposed to
predict elaborate riggable 3D face models from a single image input. FaceScape
dataset releases $16,940$ textured 3D faces, captured from $847$ subjects and
each with $20$ specific expressions. The 3D models contain the pore-level
facial geometry that is also processed to be topologically uniform. These fine
3D facial models can be represented as a 3D morphable model for coarse shapes
and displacement maps for detailed geometry. Taking advantage of the
large-scale and high-accuracy dataset, a novel algorithm is further proposed to
learn the expression-specific dynamic details using a deep neural network. The
learned relationship serves as the foundation of our 3D face prediction system
from a single image input. Different from most previous methods, our predicted
3D models are riggable with highly detailed geometry under different
expressions. We also use FaceScape data to generate the in-the-wild and
in-the-lab benchmark to evaluate recent methods of single-view face
reconstruction. The accuracy is reported and analyzed on the dimensions of
camera pose and focal length, which provides a faithful and comprehensive
evaluation and reveals new challenges. The unprecedented dataset, benchmark,
and code have been released at https://github.com/zhuhao-nju/facescape.
