# Non-Stationary Texture Synthesis by Adversarial Expansion

arXiv: https://arxiv.org/abs/1805.04487 · HTML: https://mlsanity.com/paper/1805.04487/

## Key facts

- Cited by 67 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 May 2018.

## Built on by (most cited first, 30 of 67)

- [SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations](https://mlsanity.com/paper/2108.01073/) (2021)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [InfiniteNature-Zero: Learning Perpetual View Generation of Natural Scenes from Single Images](https://mlsanity.com/paper/2207.11148/) (2022)
- [Any-resolution Training for High-resolution Image Synthesis](https://mlsanity.com/paper/2204.07156/) (2022)
- [A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly Detection](https://mlsanity.com/paper/2104.14535/) (2021)
- [Drop the GAN: In Defense of Patches Nearest Neighbors as Single Image Generative Models](https://mlsanity.com/paper/2103.15545/) (2021)
- [Patched Denoising Diffusion Models For High-Resolution Image Synthesis](https://mlsanity.com/paper/2308.01316/) (2023)
- [Generative Modelling of BRDF Textures from Flash Images](https://mlsanity.com/paper/2102.11861/) (2021)
- [Attention Distillation: A Unified Approach to Visual Characteristics Transfer](https://mlsanity.com/paper/2502.20235/) (2025)
- [Learning to Generate 3D Shapes from a Single Example](https://mlsanity.com/paper/2208.02946/) (2022)
- [Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shape](https://mlsanity.com/paper/2305.15399/) (2023)
- [Deep 3D-to-2D Watermarking: Embedding Messages in 3D Meshes and Extracting Them from 2D Renderings](https://mlsanity.com/paper/2104.13450/) (2021)
- [TexSliders: Diffusion-Based Texture Editing in CLIP Space](https://mlsanity.com/paper/2405.00672/) (2024)
- [Image Inpainting with External-internal Learning and Monochromic Bottleneck](https://mlsanity.com/paper/2104.09068/) (2021)
- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [PetsGAN: Rethinking Priors for Single Image Generation](https://mlsanity.com/paper/2203.01488/) (2022)
- [SinIR: Efficient General Image Manipulation with Single Image Reconstruction](https://mlsanity.com/paper/2106.07140/) (2021)
- [Diverse Generation from a Single Video Made Possible](https://mlsanity.com/paper/2109.08591/) (2021)
- [ShaDDR: Interactive Example-Based Geometry and Texture Generation via 3D Shape Detailization and Differentiable Rendering](https://mlsanity.com/paper/2306.04889/) (2023)
- [ExSinGAN: Learning an Explainable Generative Model from a Single Image](https://mlsanity.com/paper/2105.07350/) (2021)
- [CharacterGAN: Few-Shot Keypoint Character Animation and Reposing](https://mlsanity.com/paper/2102.03141/) (2021)
- [Texture Generation with Neural Cellular Automata](https://mlsanity.com/paper/2105.07299/) (2021)
- [TexRO: Generating Delicate Textures of 3D Models by Recursive Optimization](https://mlsanity.com/paper/2403.15009/) (2024)
- [Tiled Diffusion](https://mlsanity.com/paper/2412.15185/) (2024)
- [Image Restoration by Deep Projected GSURE](https://mlsanity.com/paper/2102.02485/) (2021)
- [Generating Novel Scene Compositions from Single Images and Videos](https://mlsanity.com/paper/2103.13389/) (2021)
- [Wasserstein Distortion: Unifying Fidelity and Realism](https://mlsanity.com/paper/2310.03629/) (2023)
- [Generating Non-Stationary Textures using Self-Rectification](https://mlsanity.com/paper/2401.02847/) (2024)
- [TexTile: A Differentiable Metric for Texture Tileability](https://mlsanity.com/paper/2403.12961/) (2024)
- [Neural Photometry-guided Visual Attribute Transfer](https://mlsanity.com/paper/2112.02520/) (2021)

## Abstract (arXiv)

The real world exhibits an abundance of non-stationary textures. Examples include textures with large-scale structures, as well as spatially variant and inhomogeneous textures. While existing example-based texture synthesis methods can cope well with stationary textures, non-stationary textures still pose a considerable challenge, which remains unresolved. In this paper, we propose a new approach for example-based non-stationary texture synthesis. Our approach uses a generative adversarial network (GAN), trained to double the spatial extent of texture blocks extracted from a specific texture exemplar. Once trained, the fully convolutional generator is able to expand the size of the entire exemplar, as well as of any of its sub-blocks. We demonstrate that this conceptually simple approach is highly effective for capturing large-scale structures, as well as other non-stationary attributes of the input exemplar. As a result, it can cope with challenging textures, which, to our knowledge, no other existing method can handle.