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We present AugurOne, a novel approach for training single image generativemodels. Our approach trains an upscaling neural network using non-affineaugmentations of the (single) input image, particularly including non-rigidthin plate spline image warps. The extensive augmentations significantlyincrease the in-sample distribution for the upsampling network enabling theupscaling of highly variable inputs. A compact latent space is jointly learnedallowing for controlled image synthesis. Differently from Single Image GAN, ourapproach does not require GAN training and takes place in an end-to-end fashionallowing fast and stable training. We experimentally evaluate our method andshow 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.

Training End-to-end Single Image Generators without GANs