  Photo-realistic visualization and animation of expressive human faces have
been a long standing challenge. 3D face modeling methods provide parametric
control but generates unrealistic images, on the other hand, generative 2D
models like GANs (Generative Adversarial Networks) output photo-realistic face
images, but lack explicit control. Recent methods gain partial control, either
by attempting to disentangle different factors in an unsupervised manner, or by
adding control post hoc to a pre-trained model. Unconditional GANs, however,
may entangle factors that are hard to undo later. We condition our generative
model on pre-defined control parameters to encourage disentanglement in the
generation process. Specifically, we condition StyleGAN2 on FLAME, a generative
3D face model. While conditioning on FLAME parameters yields unsatisfactory
results, we find that conditioning on rendered FLAME geometry and photometric
details works well. This gives us a generative 2D face model named GIF
(Generative Interpretable Faces) that offers FLAME's parametric control. Here,
interpretable refers to the semantic meaning of different parameters. Given
FLAME parameters for shape, pose, expressions, parameters for appearance,
lighting, and an additional style vector, GIF outputs photo-realistic face
images. We perform an AMT based perceptual study to quantitatively and
qualitatively evaluate how well GIF follows its conditioning. The code, data,
and trained model are publicly available for research purposes at
http://gif.is.tue.mpg.de.
