  The advance of Generative Adversarial Networks (GANs) enables realistic face
image synthesis. However, synthesizing face images that preserve facial
identity as well as have high diversity within each identity remains
challenging. To address this problem, we present FaceFeat-GAN, a novel
generative model that improves both image quality and diversity by using two
stages. Unlike existing single-stage models that map random noise to image
directly, our two-stage synthesis includes the first stage of diverse feature
generation and the second stage of feature-to-image rendering. The competitions
between generators and discriminators are carefully designed in both stages
with different objective functions. Specially, in the first stage, they compete
in the feature domain to synthesize various facial features rather than images.
In the second stage, they compete in the image domain to render photo-realistic
images that contain high diversity but preserve identity. Extensive experiments
show that FaceFeat-GAN generates images that not only retain identity
information but also have high diversity and quality, significantly
outperforming previous methods.
