  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.
