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arXiv · 2304.05139

NeAT: Neural Artistic Tracing for Beautiful Style Transfer

Dan Ruta·Andrew Gilbert·John Collomosse·Eli Shechtman·Nicholas Kolkin
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Abstract

Style transfer is the task of reproducing the semantic contents of a source image in the artistic style of a second target image. In this paper, we present NeAT, a new state-of-the art feed-forward style transfer method. We re-formulate feed-forward style transfer as image editing, rather than image generation, resulting in a model which improves over the state-of-the-art in both preserving the source content and matching the target style. An important component of our model's success is identifying and fixing "style halos", a commonly occurring artefact across many style transfer techniques. In addition to training and testing on standard datasets, we introduce the BBST-4M dataset, a new, large scale, high resolution dataset of 4M images. As a component of curating this data, we present a novel model able to classify if an image is stylistic. We use BBST-4M to improve and measure the generalization of NeAT across a huge variety of styles. Not only does NeAT offer state-of-the-art quality and generalization, it is designed and trained for fast inference at high resolution.

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Dan Ruta, Andrew Gilbert, John Collomosse, Eli Shechtman, Nicholas Kolkin. 2023-04-11. NeAT: Neural Artistic Tracing for Beautiful Style Transfer. https://arxiv.org/abs/2304.05139

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