StackGAN: Text to Photo Realistic Image Synthesis with Stacked Generative Adversarial Networks
Synthesizing high quality images from text descriptions is a challenging problem in computer vision and has many practical applications. Samples generated by existing textto 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 256.256 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.
2017 IEEE International Conference on Computer Vision (ICCV)