NeAT: Neural Artistic Tracing for Beautiful Style Transfer
This addresses the problem of generating high-quality, artifact-free style transfer for applications in digital art and media, representing an incremental advance with specific gains in performance.
The paper tackles style transfer by introducing NeAT, a feed-forward method that reformulates it as image editing to better preserve source content and match target style, achieving state-of-the-art results with improvements in quality and generalization, including training on a new 4M-image dataset.
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.