# Style Transfer by Rigid Alignment in Neural Net Feature Space

arXiv: https://arxiv.org/abs/1909.13690 · HTML: https://mlsanity.com/paper/1909.13690/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [Realtime Data-Efficient Portrait Stylization Based On Geometric Alignment](https://mlsanity.com/paper/2211.15549/) (2022)

## Abstract (arXiv)

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.