StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
This addresses the challenge of generating detailed and vivid images from text for applications in computer vision, representing a novel method rather than an incremental improvement.
The paper tackles the problem of synthesizing high-quality images from text descriptions by proposing Stacked Generative Adversarial Networks (StackGAN), which decomposes the task into sketch and refinement stages to generate 256x256 photo-realistic images, achieving significant improvements over state-of-the-art methods on benchmark datasets.
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.