Abstract
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.
Abstract (translated)
尽管生成对抗网络近年来在图像合成方面取得了迅速的进展,但目前的方法在几何域或外观域中都起作用,往往会引入各种合成伪影。本文提出了一种创新的自适应合成GaN(AC-GaN),它将几何和外观域中的图像合成结合到端到端可训练网络中,同时在两个域中实现合成现实。设计了一种新颖的层次综合机制,该机制能够在多个前景对象(无论是否有遮挡)参与合成时生成真实的几何图形和合成。此外,还引入了一种新颖的注意遮罩来指导嵌入前景对象的外观适应,有助于保持图像的细节和分辨率,并为几何领域的合成提供更好的参考。在场景文本图像合成、自动人像编辑和室内渲染任务方面的大量实验表明,所提出的AC-GAN在定性和定量上都达到了优异的合成性能。
URL
https://arxiv.org/abs/1905.04693