Despite the rapid progress of generative adversarial networks (GANs) in image synthesis in recent years, the existing image synthesis approaches work in either geometry domain or appearance domain alone which often introduces various synthesis artifacts. This paper presents an innovative Hierarchical Composition GAN (HIC-GAN) that incorporates image synthesis in geometry and appearance domains into an end-to-end trainable network and achieves superior synthesis realism in both domains simultaneously. We design an innovative hierarchical composition mechanism that is capable of learning realistic composition geometry and handling occlusions while multiple foreground objects are involved in image composition. In addition, we introduce a novel attention mask mechanism that guides to adapt the appearance of foreground objects which also helps to provide better training reference for learning in geometry domain. Extensive experiments on scene text image synthesis, portrait editing and indoor rendering tasks show that the proposed HIC-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,但现有的图像合成方法仅在几何域或外观域开展工作,往往引入各种合成文物。本文件介绍了创新的等级构成GAN(HIC-GAN),将几何和外观域的图像合成纳入端至端培训网络,同时在这两个领域实现优异的合成现实。我们设计了一个创新的等级构成机制,能够学习现实的构成几何和处理隐蔽,同时多个地表物体也参与图像的构成。此外,我们引入一个新的关注掩码机制,指导如何调整地表物体的外观,这也有助于为几何域的学习提供更好的培训参考。关于现场文字图像合成、肖像编辑和室内翻版的广泛实验表明,拟议的HIC-GAN在质量和数量上都取得了较高的合成性表现。