# Style-Transfer via Texture-Synthesis

arXiv: https://arxiv.org/abs/1609.03057 · HTML: https://mlsanity.com/paper/1609.03057/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2016.

## Built on by (most cited first, 17 of 17)

- [CLIPstyler: Image Style Transfer with a Single Text Condition](https://mlsanity.com/paper/2112.00374/) (2021)
- [Region-aware Adaptive Instance Normalization for Image Harmonization](https://mlsanity.com/paper/2106.02853/) (2021)
- [Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models](https://mlsanity.com/paper/2103.15545/) (2021)
- [Poison Ink: Robust and Invisible Backdoor Attack](https://mlsanity.com/paper/2108.02488/) (2021)
- [Robustness of SAM: Segment Anything Under Corruptions and Beyond](https://mlsanity.com/paper/2306.07713/) (2023)
- [SCS-Co: Self-Consistent Style Contrastive Learning for Image Harmonization](https://mlsanity.com/paper/2204.13962/) (2022)
- [Neural Photometry-guided Visual Attribute Transfer](https://mlsanity.com/paper/2112.02520/) (2021)
- [FastCLIPstyler: Optimisation-free Text-based Image Style Transfer Using Style Representations](https://mlsanity.com/paper/2210.03461/) (2022)
- [NeuBTF: Neural fields for BTF encoding and transfer](https://mlsanity.com/paper/2307.01199/) (2023)
- [Painterly Image Harmonization by Learning from Painterly Objects](https://mlsanity.com/paper/2312.10263/) (2023)
- [D2Styler: Advancing Arbitrary Style Transfer with Discrete Diffusion Methods](https://mlsanity.com/paper/2408.03558/) (2024)
- [MOSAIC: Multi-Object Segmented Arbitrary Stylization Using CLIP](https://mlsanity.com/paper/2309.13716/) (2023)
- [Simulation-Aided Deep Learning for Laser Ultrasonic Visualization Testing](https://mlsanity.com/paper/2305.18614/) (2023)
- [Optimal Image Transport on Sparse Dictionaries](https://mlsanity.com/paper/2311.01984/) (2023)
- [Unrestricted Adversarial Samples Based on Non-semantic Feature Clusters Substitution](https://mlsanity.com/paper/2209.02406/) (2022)
- [PP-GAN : Style Transfer from Korean Portraits to ID Photos Using Landmark Extractor with GAN](https://mlsanity.com/paper/2306.13418/) (2023)
- [SeamCam: Quantifying Seamless Camouflage via Multi-Cue Visual Detectability](https://mlsanity.com/paper/2605.16515/) (2026)

## Abstract (arXiv)

Style-transfer is a process of migrating a style from a given image to the content of another, synthesizing a new image which is an artistic mixture of the two. Recent work on this problem adopting Convolutional Neural-networks (CNN) ignited a renewed interest in this field, due to the very impressive results obtained. There exists an alternative path towards handling the style-transfer task, via generalization of texture-synthesis algorithms. This approach has been proposed over the years, but its results are typically less impressive compared to the CNN ones. In this work we propose a novel style-transfer algorithm that extends the texture-synthesis work of Kwatra et. al. (2005), while aiming to get stylized images that get closer in quality to the CNN ones. We modify Kwatra's algorithm in several key ways in order to achieve the desired transfer, with emphasis on a consistent way for keeping the content intact in selected regions, while producing hallucinated and rich style in others. The results obtained are visually pleasing and diverse, shown to be competitive with the recent CNN style-transfer algorithms. The proposed algorithm is fast and flexible, being able to process any pair of content + style images.