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

Photo-Realistic Facial Details Synthesis from Single Image