We demonstrate that it is possible to perform face-related computer vision in
the wild using synthetic data alone. The community has long enjoyed the
benefits of synthesizing training data with graphics, but the domain gap
between real and synthetic data has remained a problem, especially for human
faces. Researchers have tried to bridge this gap with data mixing, domain
adaptation, and domain-adversarial training, but we show that it is possible to
synthesize data with minimal domain gap, so that models trained on synthetic
data generalize to real in-the-wild datasets. We describe how to combine a
procedurally-generated parametric 3D face model with a comprehensive library of
hand-crafted assets to render training images with unprecedented realism and
diversity. We train machine learning systems for face-related tasks such as
landmark localization and face parsing, showing that synthetic data can both
match real data in accuracy as well as open up new approaches where manual
labelling would be impossible.

本研究通过合适的合成数据及其训练方式进行模型的学习及数据的一般化，具有现实中追随的功效及实时性。

虚假到成功：利用合成数据在野外进行面部分析

Fake It Till You Make It: Face analysis in the wild using synthetic data  alone

In this paper, we present a learning-based approach for recovering the 3D
geometry of human head from a single portrait image. Our method is learned in
an unsupervised manner without any ground-truth 3D data.
We represent the head geometry with a parametric 3D face model together with
a depth map for other head regions including hair and ear. A two-step geometry
learning scheme is proposed to learn 3D head reconstruction from in-the-wild
face images, where we first learn face shape on single images using
self-reconstruction and then learn hair and ear geometry using pairs of images
in a stereo-matching fashion. The second step is based on the output of the
first to not only improve the accuracy but also ensure the consistency of
overall head geometry.
We evaluate the accuracy of our method both in 3D and with pose manipulation
tasks on 2D images. We alter pose based on the recovered geometry and apply a
refinement network trained with adversarial learning to ameliorate the
reprojected images and translate them to the real image domain. Extensive
evaluations and comparison with previous methods show that our new method can
produce high-fidelity 3D head geometry and head pose manipulation results.

本文提供一种基于学习的方法来从单个肖像图像中恢复人头的 3D 几何形状，并且使用参数化 3D 人脸模型来表示头部几何形状以及其他头部区域的深度图，同时使用双目立体匹配方法从具有野外人脸的图像中学习头发和耳朵的几何信息，最后，评估和对比结果表明该方法可以产生高保真的 3D 头部几何形态和头部姿态操作结果。