# Continuous Curve Textures

arXiv: https://arxiv.org/abs/2012.07959 · HTML: https://mlsanity.com/paper/2012.07959/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Dec 2020.

## Built on by (most cited first, 4 of 4)

- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [SketchHairSalon: Deep Sketch-based Hair Image Synthesis](https://mlsanity.com/paper/2109.07874/) (2021)
- [Structured Pattern Expansion with Diffusion Models](https://mlsanity.com/paper/2411.08930/) (2024)
- [By-Example Synthesis of Vector Textures](https://mlsanity.com/paper/2501.13812/) (2025)

## Abstract (arXiv)

Repetitive patterns are ubiquitous in natural and human-made objects, and can be created with a variety of tools and methods. Manual authoring provides unmatched degree of freedom and control, but can require significant artistic expertise and manual labor. Computational methods can automate parts of the manual creation process, but are mainly tailored for discrete pixels or elements instead of more general continuous structures. We propose an example-based method to synthesize continuous curve patterns from exemplars. Our main idea is to extend prior sample-based discrete element synthesis methods to consider not only sample positions (geometry) but also their connections (topology). Since continuous structures can exhibit higher complexity than discrete elements, we also propose robust, hierarchical synthesis to enhance output quality. Our algorithm can generate a variety of continuous curve patterns fully automatically. For further quality improvement and customization, we also present an autocomplete user interface to facilitate interactive creation and iterative editing. We evaluate our methods and interface via different patterns, ablation studies, and comparisons with alternative methods.