SinGAN trains a generative adversarial network on a single natural image, learning to synthesize random samples, animation sequences, and perform image manipulation tasks like harmonization and super-resolution. The model learns internal statistics of the image at multiple scales to generate realistic variations. This approach won the Marr Prize at ICCV 2019 as best paper.