Photorealistic Style Transfer
with Screened Poisson Equation
[Paper] [GitHub] [Results] [RealismNet Results]
Classic style-transfer methods take an input image (a) and a reference style image
(b) and produce a stylized image (c), typically showing texture artifacts and missing details
that make it look like a painting. Our method processes the stylized image (c) and makes it
photo-realistic (d). The identity of the original image is preserved while the desired style is
reliably transfered. The styled images were produced by StyleSwap  (top) and NeuralStyle
 (bottom). Best seen enlarged on a full screen.
Recent work has shown impressive success in transferring painterly style to images.
These approaches, however, fall short of photorealistic style transfer. Even when both
the input and reference images are photographs, the output still exhibits distortions reminiscent
of a painting. In this paper we propose an approach that takes as input a stylized
image and makes it more photorealistic. It relies on the Screened Poisson Equation,
maintaining the fidelity of the stylized image while constraining the gradients to those of
the original input image. Our method is fast, simple, fully automatic and shows positive
progress in making an image photorealistic. Our stylized images exhibit finer details and
are less prone to artifacts.
“Photorealistic Style Transfer with Screened Poisson Equation”, to appear in BMVC 2017
Try our code
Code to reporduce the experiments described in our paper is available in
Recent Related Work
Deep photo style transfer
Fujun Luan, Sylvain Paris, Eli Shechtman, and Kavita Bala In IEEE CVPR, 2017.