  Current GAN-based art generation methods produce unoriginal artwork due to
their dependence on conditional input. Here, we propose Sketch-And-Paint GAN
(SAPGAN), the first model which generates Chinese landscape paintings from end
to end, without conditional input. SAPGAN is composed of two GANs: SketchGAN
for generation of edge maps, and PaintGAN for subsequent edge-to-painting
translation. Our model is trained on a new dataset of traditional Chinese
landscape paintings never before used for generative research. A 242-person
Visual Turing Test study reveals that SAPGAN paintings are mistaken as human
artwork with 55% frequency, significantly outperforming paintings from baseline
GANs. Our work lays a groundwork for truly machine-original art generation.
