Despite recent success in conditional image synthesis, prevalent input conditions such as semantics and edges are not clear enough to express `Linear (Ridges)' and `Planar (Scale)' representations. To address this problem, we propose a novel framework StyLandGAN, which synthesizes desired landscape images using a depth map which has higher expressive power. Our StyleLandGAN is extended from the unconditional generation model to accept input conditions. We also propose a '2-phase inference' pipeline which generates diverse depth maps and shifts local parts so that it can easily reflect user's intend. As a comparison, we modified the existing semantic image synthesis models to accept a depth map as well. Experimental results show that our method is superior to existing methods in quality, diversity, and depth-accuracy.

翻译：尽管最近有条件图像合成取得了成功,但语义学和边缘等普遍输入条件不够清楚,不足以表达“利纳尔(里杰斯)”和“平面(规模)”的表示。为了解决这一问题,我们提议了一个新颖的框架StyLandGAN,它利用深度地图综合了理想的景观图像,而深度地图的表达力更高。我们的“风格LandGAN”从无条件的生成模型扩大到接受输入条件。我们还提议了一个“2-相位推断”管道,该管道生成不同的深度地图,并移动本地部件,以便很容易反映用户的意图。相比之下,我们修改了现有的语义图像合成模型,以接受深度地图。实验结果表明,我们的方法优于质量、多样性和深度准确性方面的现有方法。