  Although Generative Adversarial Networks (GANs) have made significant
progress in face synthesis, there lacks enough understanding of what GANs have
learned in the latent representation to map a random code to a photo-realistic
image. In this work, we propose a framework called InterFaceGAN to interpret
the disentangled face representation learned by the state-of-the-art GAN models
and study the properties of the facial semantics encoded in the latent space.
We first find that GANs learn various semantics in some linear subspaces of the
latent space. After identifying these subspaces, we can realistically
manipulate the corresponding facial attributes without retraining the model. We
then conduct a detailed study on the correlation between different semantics
and manage to better disentangle them via subspace projection, resulting in
more precise control of the attribute manipulation. Besides manipulating the
gender, age, expression, and presence of eyeglasses, we can even alter the face
pose and fix the artifacts accidentally made by GANs. Furthermore, we perform
an in-depth face identity analysis and a layer-wise analysis to evaluate the
editing results quantitatively. Finally, we apply our approach to real face
editing by employing GAN inversion approaches and explicitly training
feed-forward models based on the synthetic data established by InterFaceGAN.
Extensive experimental results suggest that learning to synthesize faces
spontaneously brings a disentangled and controllable face representation.
