  In this paper, we present a large-scale detailed 3D face dataset, FaceScape,
and propose a novel algorithm that is able to predict elaborate riggable 3D
face models from a single image input. FaceScape dataset provides 18,760
textured 3D faces, captured from 938 subjects and each with 20 specific
expressions. The 3D models contain the pore-level facial geometry that is also
processed to be topologically uniformed. These fine 3D facial models can be
represented as a 3D morphable model for rough 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 than the previous methods, our predicted 3D models are riggable with
highly detailed geometry under different expressions. The unprecedented dataset
and code will be released to public for research purpose.
