Neural style transfer is a deep learning technique that produces an
unprecedentedly rich style transfer from a style image to a content image and
is particularly impressive when it comes to transferring style from a painting
to an image. It was originally achieved by solving an optimization problem to
match the global style statistics of the style image while preserving the local
geometric features of the content image. The two main drawbacks of this
original approach is that it is computationally expensive and that the
resolution of the output images is limited by high GPU memory requirements.
Many solutions have been proposed to both accelerate neural style transfer and
increase its resolution, but they all compromise the quality of the produced
images. Indeed, transferring the style of a painting is a complex task
involving features at different scales, from the color palette and
compositional style to the fine brushstrokes and texture of the canvas. This
paper provides a solution to solve the original global optimization for
ultra-high resolution images, enabling multiscale style transfer at
unprecedented image sizes. This is achieved by spatially localizing the
computation of each forward and backward passes through the VGG network.
Extensive qualitative and quantitative comparisons show that our method
produces a style transfer of unmatched quality for such high resolution
painting styles.Comment: 10 pages, 5 figure