Style Transfer by Rigid Alignment in Neural Net Feature Space
This work addresses the challenge of maintaining content integrity while transferring style in images, which is important for applications in graphics and media, though it appears incremental as it builds on existing feature transformation methods.
The paper tackles the problem of arbitrary style transfer in computer vision, where existing methods compromise visual quality or content structure, and presents an approach using rigid alignment in feature space to generate high-quality stylized images with intact content structure.
Arbitrary style transfer is an important problem in computer vision that aims to transfer style patterns from an arbitrary style image to a given content image. However, current methods either rely on slow iterative optimization or fast pre-determined feature transformation, but at the cost of compromised visual quality of the styled image; especially, distorted content structure. In this work, we present an effective and efficient approach for arbitrary style transfer that seamlessly transfers style patterns as well as keep content structure intact in the styled image. We achieve this by aligning style features to content features using rigid alignment; thus modifying style features, unlike the existing methods that do the opposite. We demonstrate the effectiveness of the proposed approach by generating high-quality stylized images and compare the results with the current state-of-the-art techniques for arbitrary style transfer.