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 model to generate plausible images of birds and flowers from detailed text descriptions.
In contemporary work generated images from text captions, using a variational recurrent autoencoder with attention to paint the image in multiple steps, similar to DRAW @cite_9 . Impressively, the model can perform reasonable synthesis of completely novel (unlikely for a human to write) text such as a stop sign is flying in blue skies'', suggesting that it does not simply memorize. While the results are encouraging, the problem is highly challenging and the generated images are not yet realistic, i.e., mistakeable for real. Our model can in many cases generate visually-plausible @math images conditioned on text, and is also distinct in that our entire model is a GAN, rather only using GAN for post-processing.
@cite_9
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This paper introduces the Deep Recurrent Attentive Writer (DRAW) neural network architecture for image generation. DRAW networks combine a novel spatial attention mechanism that mimics the foveation of the human eye, with a sequential variational auto-encoding framework that allows for the iterative construction of complex images. The system substantially improves on the state of the art for generative models on MNIST, and, when trained on the Street View House Numbers dataset, it generates images that cannot be distinguished from real data with the naked eye.