Referenceless User Controllable Semantic Image Synthesis
Jonghyun Kim Gen Li Joongkyu Kim
Despite recent progress in semantic image synthesis, complete control over
image style remains a challenging problem. Existing methods require reference
images to feed style information into semantic layouts, which indicates that
the style is constrained by the given image. In this paper, we propose a model
named RUCGAN for user controllable semantic image synthesis, which utilizes a
singular color to represent the style of a specific semantic region. The
proposed network achieves reference-free semantic image synthesis by injecting
color as user-desired styles into each semantic layout, and is able to
synthesize semantic images with unusual colors. Extensive experimental results
on various challenging datasets show that the proposed method outperforms
existing methods, and we further provide an interactive UI to demonstrate the
advantage of our approach for style controllability.