Training End to end Single Image Generators without GANs
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.
ArXiv