Photo-Realistic Facial Details Synthesis From Single Image

Anpei Chen, Zhang Chen, Guli Zhang, Kenny Mitchell, Jingyi Yu; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2019, pp. 9429-9439

Abstract


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 163K 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.

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[bibtex]
@InProceedings{Chen_2019_ICCV,
author = {Chen, Anpei and Chen, Zhang and Zhang, Guli and Mitchell, Kenny and Yu, Jingyi},
title = {Photo-Realistic Facial Details Synthesis From Single Image},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}