Adaptive Composition GAN towards Realistic Image Synthesis

Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, current approaches work in either geometry domain or appearance domain which tend to introduce various synthesis artifacts. This paper presents an innovative Adaptive Composition GAN (AC-GAN) that incorporates image synthesis in geometry and appearance domains into an end-to-end trainable network and achieves synthesis realism in both domains simultaneously. An innovative hierarchical synthesis mechanism is designed which is capable of generating realistic geometry and composition when multiple foreground objects with or without occlusions are involved in synthesis. In addition, a novel attention mask is introduced to guide the appearance adaptation to the embedded foreground objects which helps preserve image details and resolution and also provide better reference for synthesis in geometry domain. Extensive experiments on scene text image synthesis, automated portrait editing and indoor rendering tasks show that the proposed AC-GAN achieves superior synthesis performance qualitatively and quantitatively.