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.

本文介绍了一种名为 InterFaceGAN 的框架，通过解析状态最先进的 GAN 模型学习的分离面部表示，并研究在潜在空间中编码的面部语义属性特性，可以实现面部属性的真实操作而不必重新训练模型，并且可以更准确地控制特征操作。

InterFaceGAN：解读 GAN 学习的分解人脸表示

InterFaceGAN: Interpreting the Disentangled Face Representation Learned  by GANs

Despite the recent advance of Generative Adversarial Networks (GANs) in
high-fidelity image synthesis, there lacks enough understanding of how GANs are
able to map a latent code sampled from a random distribution to a
photo-realistic image. Previous work assumes the latent space learned by GANs
follows a distributed representation but observes the vector arithmetic
phenomenon. In this work, we propose a novel framework, called InterFaceGAN,
for semantic face editing by interpreting the latent semantics learned by GANs.
In this framework, we conduct a detailed study on how different semantics are
encoded in the latent space of GANs for face synthesis. We find that the latent
code of well-trained generative models actually learns a disentangled
representation after linear transformations. We explore the disentanglement
between various semantics and manage to decouple some entangled semantics with
subspace projection, leading to more precise control of facial attributes.
Besides manipulating gender, age, expression, and the presence of eyeglasses,
we can even vary the face pose as well as fix the artifacts accidentally
generated by GAN models. The proposed method is further applied to achieve real
image manipulation when combined with GAN inversion methods or some
encoder-involved models. Extensive results suggest that learning to synthesize
faces spontaneously brings a disentangled and controllable facial attribute
representation.

本文提出了一个名为 InterFaceGAN 的新框架，用于解释 GAN 学习到的语义面部编辑，并深入研究不同语义如何编码在 GAN 的潜在空间中。我们发现，精心训练的生成模型的潜在编码实际上学习了一个分离的表示，在线性变换后解旋，通过子空间投影来解开某些绑定属性，实现更精确的面部特征控制，并产生可控的面部属性表示的结果表明 GAN 具有生成高保真图像的能力。