Title: GP-GAN: Towards Realistic High-Resolution Image Blending,
Abstract: Recent advances in generative adversarial networks (GANs) have shown
promising potentials in conditional image generation. However, how to generate
high-resolution images remains an open problem. In this paper, we aim at
generating high-resolution well-blended images given composited copy-and-paste
ones, i.e. realistic high-resolution image blending. To achieve this goal, we
propose Gaussian-Poisson GAN (GP-GAN), a framework that combines the strengths
of classical gradient-based approaches and GANs, which is the first work that
explores the capability of GANs in high-resolution image blending task to the
best of our knowledge. Particularly, we propose Gaussian-Poisson Equation to
formulate the high-resolution image blending problem, which is a joint
optimisation constrained by the gradient and colour information. Gradient
filters can obtain gradient information. For generating the colour information,
we propose Blending GAN to learn the mapping between the composited image and
the well-blended one. Compared to the alternative methods, our approach can
deliver high-resolution, realistic images with fewer bleedings and unpleasant
artefacts. Experiments confirm that our approach achieves the state-of-the-art
performance on Transient Attributes dataset. A user study on Amazon Mechanical
Turk finds that majority of workers are in favour of the proposed approach.