  We present a single-image 3D face synthesis technique that can handle
challenging facial expressions while recovering fine geometric details. Our
technique employs expression analysis for proxy face geometry generation and
combines supervised and unsupervised learning for facial detail synthesis. On
proxy generation, we conduct emotion prediction to determine a new
expression-informed proxy. On detail synthesis, we present a Deep Facial Detail
Net (DFDN) based on Conditional Generative Adversarial Net (CGAN) that employs
both geometry and appearance loss functions. For geometry, we capture 366
high-quality 3D scans from 122 different subjects under 3 facial expressions.
For appearance, we use additional 20K in-the-wild face images and apply
image-based rendering to accommodate lighting variations. Comprehensive
experiments demonstrate that our framework can produce high-quality 3D faces
with realistic details under challenging facial expressions.
