mlsanityData to 7 Oct 2026

TileGAN: Synthesis of Large-Scale Non-Homogeneous Textures

arXiv 1904.12795 ↗First posted 29 Apr 2019cs.GR cs.AI cs.CVcited by 15

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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.

Key facts. Cited by 15 papers in the map (data through 7 Oct 2026). First posted on arXiv 29 Apr 2019.

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Citing papers per year: 2021: 4; 2022: 5; 2023: 2; 2024: 3; 2025: 1.

15 papers.

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  1. 2022InsetGAN for Full-Body Image Generationcited by 45
  2. 2022SeamlessGAN: Self-Supervised Synthesis of Tileable Texture Mapscited by 19
  3. 2021Barbershop: GAN-based Image Compositing using Segmentation Maskscited by 14
  4. 2021Texture Reformer: Towards Fast and Universal Interactive Texture Transfercited by 12
  5. 2021Neural Photometry-guided Visual Attribute Transfercited by 6
  6. 2023NeuBTF: Neural fields for BTF encoding and transfercited by 5
  7. 2022Synthesizing Photorealistic Virtual Humans Through Cross-modal Disentanglementcited by 4
  8. 2024Compositional Neural Texturescited by 4
  9. 2022Projective Urban Texturingcited by 3
  10. 2022SelectionConv: Convolutional Neural Networks for Non-rectilinear Image Datacited by 3
  11. 2023Directional Texture Editing for 3D Modelscited by 3
  12. 2024Learning Images Across Scales Using Adversarial Trainingcited by 3
3 more
  1. 2021Seamless Satellite-image Synthesiscited by 2
  2. 2024Content-aware Tile Generation using Exterior Boundary Inpaintingcited by 2
  3. 2025InfiniteDiffusion: Bridging Learned Fidelity and Procedural Utility for Open-World Terrain Generationcited by 1