Diverse single image generation with controllable global structure

Abstract

Image generation from a single image using generative adversarial networks is quite interesting due to the realism of generated images. However, recent approaches need improvement for such realistic and diverse image generation, when the global context of the image is important such as in face, animal, and architectural image generation. This is mainly due to the use of fewer convolutional layers for capturing the patch statistics and, thereby, not being able to capture global statistics well. The challenge, then, is to preserve the global structure, while retaining the diversity and quality of image generation. We solve this problem by using attention blocks at selected scales and feeding a random Gaussian blurred image to the discriminator for training. We use adversarial feedback to make the quality of the generation better. Our results are visually better than the state-of-the-art, particularly, in generating images that require global context. The diversity of our image generation, measured using the average standard deviation of pixels, is also better.