“End-To-End Chinese Landscape Painting Creation Using Generative Adversarial Networks”, 2020-11-11 (; backlinks; similar):
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, outperforming paintings from baseline GANs.
Our work lays a groundwork for truly machine-original art generation.