  In this paper we present a compositing image synthesis method that generates
RGB canvases with well aligned segmentation maps and sparse depth maps, coupled
with an in-painting network that transforms the RGB canvases into high quality
RGB images and the sparse depth maps into pixel-wise dense depth maps. We
benchmark our method in terms of structural alignment and image quality,
showing an increase in mIoU over SOTA by 3.7 percentage points and a highly
competitive FID. Furthermore, we analyse the quality of the generated data as
training data for semantic segmentation and depth completion, and show that our
approach is more suited for this purpose than other methods.
