  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.
