  Generating an image from a given text description has two goals: visual
realism and semantic consistency. Although significant progress has been made
in generating high-quality and visually realistic images using generative
adversarial networks, guaranteeing semantic consistency between the text
description and visual content remains very challenging. In this paper, we
address this problem by proposing a novel global-local attentive and
semantic-preserving text-to-image-to-text framework called MirrorGAN. MirrorGAN
exploits the idea of learning text-to-image generation by redescription and
consists of three modules: a semantic text embedding module (STEM), a
global-local collaborative attentive module for cascaded image generation
(GLAM), and a semantic text regeneration and alignment module (STREAM). STEM
generates word- and sentence-level embeddings. GLAM has a cascaded architecture
for generating target images from coarse to fine scales, leveraging both local
word attention and global sentence attention to progressively enhance the
diversity and semantic consistency of the generated images. STREAM seeks to
regenerate the text description from the generated image, which semantically
aligns with the given text description. Thorough experiments on two public
benchmark datasets demonstrate the superiority of MirrorGAN over other
representative state-of-the-art methods.
