Title: StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks,
Abstract: Although Generative Adversarial Networks (GANs) have shown remarkable success
in various tasks, they still face challenges in generating high quality images.
In this paper, we propose Stacked Generative Adversarial Networks (StackGAN)
aiming at generating high-resolution photo-realistic images. First, we propose
a two-stage generative adversarial network architecture, StackGAN-v1, for
text-to-image synthesis. The Stage-I GAN sketches the primitive shape and
colors of the object based on given text description, yielding low-resolution
images. The Stage-II GAN takes Stage-I results and text descriptions as inputs,
and generates high-resolution images with photo-realistic details. Second, an
advanced multi-stage generative adversarial network architecture, StackGAN-v2,
is proposed for both conditional and unconditional generative tasks. Our
StackGAN-v2 consists of multiple generators and discriminators in a tree-like
structure; images at multiple scales corresponding to the same scene are
generated from different branches of the tree. StackGAN-v2 shows more stable
training behavior than StackGAN-v1 by jointly approximating multiple
distributions. Extensive experiments demonstrate that the proposed stacked
generative adversarial networks significantly outperform other state-of-the-art
methods in generating photo-realistic images.