mlsanityData to 7 Oct 2026

By-Example Synthesis of Vector Textures

arXiv 2501.13812 ↗First posted 23 Jan 2025cs.CV cs.GRcited by 0

Not on the map. Why?

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 12 of its 54 references that are in the map. First posted on arXiv 23 Jan 2025.

Builds on

12 of 54 references.

References · 12

  1. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  2. 2023Segment Anythingcited by 8,158
  3. 2022Exploring CLIP for Assessing the Look and Feel of Imagescited by 974
  4. 2020Image Quality Assessment: Unifying Structure and Texture Similaritycited by 792
  5. 2019SinGAN: Learning a Generative Model from a Single Natural Imagecited by 348
  6. 2018PieAPP: Perceptual Image-Error Assessment through Pairwise Preferencecited by 158
  7. 2018Non-Stationary Texture Synthesis by Adversarial Expansioncited by 66
  8. 2017Learning Texture Manifolds with the Periodic Spatial GANcited by 53
  9. 2016Texture Synthesis with Spatial Generative Adversarial Networkscited by 45
  10. 2024TexTile: A Differentiable Metric for Texture Tileabilitycited by 7
  11. 2020Continuous Curve Texturescited by 4
  12. 2023Learning Gradient Fields for Scalable and Generalizable Irregular Packingcited by 3

Built on by

Not cited yet.