University of Michigan - Ann Arbor

The authors of this paper propose a solution to the problem of synthesizing high-quality images from text descriptions in computer vision. They propose Stacked Generative Adversarial Networks (StackGAN) to generate 256x256 photo-realistic images conditioned on text descriptions. They decompose the hard problem into more manageable sub-problems through a sketch-refinement process.

StackGAN

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.

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