  We present AugurOne, a novel approach for training single image generative
models. Our approach trains an upscaling neural network using non-affine
augmentations of the (single) input image, particularly including non-rigid
thin plate spline image warps. The extensive augmentations significantly
increase the in-sample distribution for the upsampling network enabling the
upscaling of highly variable inputs. A compact latent space is jointly learned
allowing for controlled image synthesis. Differently from Single Image GAN, our
approach does not require GAN training and takes place in an end-to-end fashion
allowing fast and stable training. We experimentally evaluate our method and
show that it obtains compelling novel animations of single-image, as well as,
state-of-the-art performance on conditional generation tasks e.g.
paint-to-image and edges-to-image.
