Generative Adversarial Text to Image Synthesis

Automatic synthesis of realistic images from text would be interesting and useful, but current AI systems are still far from this goal. However, in recent years generic and powerful recurrent neural network architectures have been developed to learn discriminative text feature representations. Meanwhile, deep convolutional generative adversarial networks (GANs) have begun to generate highly compelling images of specific categories, such as faces, album covers, and room interiors. In this work, we develop a novel deep architecture and GAN formulation to effectively bridge these advances in text and image model- ing, translating visual concepts from characters to pixels. We demonstrate the capability of our…

• NS
  National Science Foundation

  Awards: 1266184, CMMI-1266184, 1453651, CAREER
• OO
  Office of Naval Research

  Award: N00014
• DO
  Division of Civil, Mechanical and Manufacturing Innovation

  Awards: CMMI-1266184, CAREER