  Photorealistic style transfer is the task of transferring the artistic style
of an image onto a content target, producing a result that is plausibly taken
with a camera. Recent approaches, based on deep neural networks, produce
impressive results but are either too slow to run at practical resolutions, or
still contain objectionable artifacts. We propose a new end-to-end model for
photorealistic style transfer that is both fast and inherently generates
photorealistic results. The core of our approach is a feed-forward neural
network that learns local edge-aware affine transforms that automatically obey
the photorealism constraint. When trained on a diverse set of images and a
variety of styles, our model can robustly apply style transfer to an arbitrary
pair of input images. Compared to the state of the art, our method produces
visually superior results and is three orders of magnitude faster, enabling
real-time performance at 4K on a mobile phone. We validate our method with
ablation and user studies.
