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.

翻译：尽管近年来图像合成的基因对抗网络(GANs)取得了快速进展,但目前的做法在几何域或外观域开展工作,往往采用各种合成文物,本文件介绍了创新的适应性合成GAN(AC-GAN),将几何和外观域的图像合成纳入端到端到端的可培训网络,同时在这两个领域实现合成现实主义。设计了一个创新的等级合成机制,能够在有或无隔离的多个地表物体参与合成时产生现实的几何和构成。此外,还引入了新的关注面罩,以指导嵌入的地表物体的外观适应,帮助保存图像细节和分辨率,并为几何域的合成提供更好的参考。关于现场文本合成、自动肖像编辑和室内翻版的广泛实验表明,拟议的AC-GAN在质量和数量上都取得了较高的合成性表现。