Training End-to-end Single Image Generators without GANs
This provides a fast and stable alternative to GAN-based methods for single image generation, benefiting researchers and practitioners in computer vision and graphics.
The paper tackles the problem of training single image generative models without GANs by introducing AugurOne, which uses non-affine augmentations like thin plate spline warps to train an upscaling network, achieving state-of-the-art performance on tasks such as paint-to-image and edges-to-image.
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.