  Synthesizing high-quality images from text descriptions is a challenging
problem in computer vision and has many practical applications. Samples
generated by existing text-to-image approaches can roughly reflect the meaning
of the given descriptions, but they fail to contain necessary details and vivid
object parts. In this paper, we propose Stacked Generative Adversarial Networks
(StackGAN) to generate 256x256 photo-realistic images conditioned on text
descriptions. We decompose the hard problem into more manageable sub-problems
through a sketch-refinement process. The Stage-I GAN sketches the primitive
shape and colors of the object based on the given text description, yielding
Stage-I 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. It is able to rectify defects in Stage-I results and
add compelling details with the refinement process. To improve the diversity of
the synthesized images and stabilize the training of the conditional-GAN, we
introduce a novel Conditioning Augmentation technique that encourages
smoothness in the latent conditioning manifold. Extensive experiments and
comparisons with state-of-the-arts on benchmark datasets demonstrate that the
proposed method achieves significant improvements on generating photo-realistic
images conditioned on text descriptions.
