# By-Example Synthesis of Vector Textures

arXiv: https://arxiv.org/abs/2501.13812 · HTML: https://mlsanity.com/paper/2501.13812/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 54 references that are in the map.
- First posted on arXiv 23 Jan 2025.

## Builds on (most cited first, 12 of 12)

- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Exploring CLIP for Assessing the Look and Feel of Images](https://mlsanity.com/paper/2207.12396/) (2022)
- [Image Quality Assessment: Unifying Structure and Texture Similarity](https://mlsanity.com/paper/2004.07728/) (2020)
- [SinGAN: Learning a Generative Model from a Single Natural Image](https://mlsanity.com/paper/1905.01164/) (2019)
- [PieAPP: Perceptual Image-Error Assessment through Pairwise Preference](https://mlsanity.com/paper/1806.02067/) (2018)
- [Non-Stationary Texture Synthesis by Adversarial Expansion](https://mlsanity.com/paper/1805.04487/) (2018)
- [Learning Texture Manifolds with the Periodic Spatial GAN](https://mlsanity.com/paper/1705.06566/) (2017)
- [Texture Synthesis with Spatial Generative Adversarial Networks](https://mlsanity.com/paper/1611.08207/) (2016)
- [TexTile: A Differentiable Metric for Texture Tileability](https://mlsanity.com/paper/2403.12961/) (2024)
- [Continuous Curve Textures](https://mlsanity.com/paper/2012.07959/) (2020)
- [Learning Gradient Fields for Scalable and Generalizable Irregular Packing](https://mlsanity.com/paper/2310.19814/) (2023)

## Abstract (arXiv)

We propose a new method for synthesizing an arbitrarily sized novel vector texture given a single raster exemplar. Our method first segments the exemplar to extract the primary textons, and then clusters them based on visual similarity. We then compute a descriptor to capture each texton's neighborhood which contains the inter-category relationships that are used at synthesis time. Next, we use a simple procedure to both extract and place the secondary textons behind the primary polygons. Finally, our method constructs a gradient field for the background which is defined by a set of data points and colors. The color of the secondary polygons are also adjusted to better match the gradient field. To compare our work with other methods, we use a wide range of perceptual-based metrics.