# TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

arXiv: https://arxiv.org/abs/1904.12795 · HTML: https://mlsanity.com/paper/1904.12795/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Apr 2019.

## Built on by (most cited first, 15 of 15)

- [InsetGAN for Full-Body Image Generation](https://mlsanity.com/paper/2203.07293/) (2022)
- [SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Maps](https://mlsanity.com/paper/2201.05120/) (2022)
- [Barbershop: GAN-based Image Compositing using Segmentation Masks](https://mlsanity.com/paper/2106.01505/) (2021)
- [Texture Reformer: Towards Fast and Universal Interactive Texture Transfer](https://mlsanity.com/paper/2112.02788/) (2021)
- [Neural Photometry-guided Visual Attribute Transfer](https://mlsanity.com/paper/2112.02520/) (2021)
- [NeuBTF: Neural fields for BTF encoding and transfer](https://mlsanity.com/paper/2307.01199/) (2023)
- [Compositional Neural Textures](https://mlsanity.com/paper/2404.12509/) (2024)
- [Synthesizing Photorealistic Virtual Humans Through Cross-modal Disentanglement](https://mlsanity.com/paper/2209.01320/) (2022)
- [Projective Urban Texturing](https://mlsanity.com/paper/2201.10938/) (2022)
- [SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Data](https://mlsanity.com/paper/2207.08979/) (2022)
- [Directional Texture Editing for 3D Models](https://mlsanity.com/paper/2309.14872/) (2023)
- [Learning Images Across Scales Using Adversarial Training](https://mlsanity.com/paper/2406.08924/) (2024)
- [Seamless Satellite-image Synthesis](https://mlsanity.com/paper/2111.03384/) (2021)
- [Content-aware Tile Generation using Exterior Boundary Inpainting](https://mlsanity.com/paper/2409.14184/) (2024)
- [InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generation](https://mlsanity.com/paper/2512.08309/) (2025)

## Abstract (arXiv)

We tackle the problem of texture synthesis in the setting where many input images are given and a large-scale output is required. We build on recent generative adversarial networks and propose two extensions in this paper. First, we propose an algorithm to combine outputs of GANs trained on a smaller resolution to produce a large-scale plausible texture map with virtually no boundary artifacts. Second, we propose a user interface to enable artistic control. Our quantitative and qualitative results showcase the generation of synthesized high-resolution maps consisting of up to hundreds of megapixels as a case in point.