What kind of algorithm is used by StackGAN to generate realistic images from text? How does StackGAN work?


The paper StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks should provide the answers to your questions.

Here's an excerpt from the abstract of the paper.

Synthesizing photo-realistic 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 photo-realistic images conditioned on text descriptions. The Stage-I GAN sketches the primitive shape and basic 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 photorealistic details. The Stage-II GAN is able to rectify defects and add compelling details with the refinement process.


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