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In this paper, we investigate deep image synthesis guided by sketch, color,and texture. Previous image synthesis methods can be controlled by sketch andcolor strokes but we are the first to examine texture control. We allow a userto place a texture patch on a sketch at arbitrary locations and scales tocontrol the desired output texture. Our generative network learns to synthesizeobjects consistent with these texture suggestions. To achieve this, we developa local texture loss in addition to adversarial and content loss to train thegenerative network. We conduct experiments using sketches generated from realimages and textures sampled from a separate texture database and results showthat our proposed algorithm is able to generate plausible images that arefaithful to user controls. Ablation studies show that our proposed pipeline cangenerate more realistic images than adapting existing methods directly.

TextureGAN: Controlling Deep Image Synthesis with Texture Patches