mlsanityData to 7 Oct 2026

Infinite Texture: Text-guided High Resolution Diffusion Texture Synthesis

arXiv 2405.08210 ↗First posted 13 May 2024cs.CVcited by 5

On the map: Diffusion models → 3D, point cloud and scene generation

Abstract (arXiv)

We present Infinite Texture, a method for generating arbitrarily large texture images from a text prompt. Our approach fine-tunes a diffusion model on a single texture, and learns to embed that statistical distribution in the output domain of the model. We seed this fine-tuning process with a sample texture patch, which can be optionally generated from a text-to-image model like DALL-E 2. At generation time, our fine-tuned diffusion model is used through a score aggregation strategy to generate output texture images of arbitrary resolution on a single GPU. We compare synthesized textures from our method to existing work in patch-based and deep learning texture synthesis methods. We also showcase two applications of our generated textures in 3D rendering and texture transfer.

Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Placed in 3D, point cloud and scene generation, one of 14 directions in Diffusion models. Builds on 31 of its 49 references that are in the map. First posted on arXiv 13 May 2024.

Builds on

31 of 49 references.

References · 31

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2019Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformercited by 14,491
  6. 2020Denoising Diffusion Implicit Modelscited by 9,655
  7. 2021Diffusion Models Beat GANs on Image Synthesiscited by 8,185
  8. 2022Hierarchical Text-Conditional Image Generation with CLIP Latentscited by 6,539
  9. 2022Photorealistic Text-to-Image Diffusion Models with Deep Language Understandingcited by 6,322
  10. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  11. 2021GLIDE: Towards Photorealistic Image Generation and Editing with Text-Guided Diffusion Modelscited by 3,595
  12. 2022DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generationcited by 3,286
19 more
  1. 2016Perceptual Losses for Real-Time Style Transfer and Super-Resolutioncited by 2,678
  2. 2022DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Stepscited by 1,961
  3. 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,484
  4. 2016Generative Adversarial Text to Image Synthesiscited by 735
  5. 2022On Distillation of Guided Diffusion Modelscited by 721
  6. 2017AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networkscited by 664
  7. 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 529
  8. 2015A Neural Algorithm of Artistic Stylecited by 480
  9. 2019DM-GAN: Dynamic Memory Generative Adversarial Networks for Text-to-Image Synthesiscited by 308
  10. 2017Improved Texture Networks: Maximizing Quality and Diversity in Feed-forward Stylization and Texture Synthesiscited by 215
  11. 2016Texture Networks: Feed-forward Synthesis of Textures and Stylized Imagescited by 173
  12. 2020DF-GAN: A Simple and Effective Baseline for Text-to-Image Synthesiscited by 161
  13. 2022Pretraining is All You Need for Image-to-Image Translationcited by 160
  14. 2015Generating Images from Captions with Attentioncited by 135
  15. 2018Non-Stationary Texture Synthesis by Adversarial Expansioncited by 66
  16. 2017Learning Texture Manifolds with the Periodic Spatial GANcited by 53
  17. 2016Texture Synthesis with Spatial Generative Adversarial Networkscited by 45
  18. 2021μNCA: Texture Generation with Ultra-Compact Neural Cellular Automatacited by 13
  19. 2020Unique Geometry and Texture from Corresponding Image Patchescited by 1

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 1; 2025: 2; 2026: 2.

5 papers.

  1. 2024Sketch2Scene: Automatic Generation of Interactive 3D Game Scenes from User's Casual Sketchescited by 21
  2. 2026Perceptual misalignment of texture representations in convolutional neural networkscited by 2
  3. 2025UltraZoom: Generating Gigapixel Images from Regular Photoscited by 1
  4. 2025Example-Based Feature Painting on Texturescited by 1
  5. 2026GarmentZoom: Generating Zoomable Images from Garment Listingscited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2024GenesisTex2: Stable, Consistent and High-Quality Text-to-Texture Generation
  2. 2023TEXTure: Text-Guided Texturing of 3D Shapes
  3. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Models
  4. 2023TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models
  5. 2023Text-Guided Texturing by Synchronized Multi-View Diffusion
  6. 2025CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shading
  7. 2023Text-guided High-definition Consistency Texture Model
  8. 2024RoCoTex: A Robust Method for Consistent Texture Synthesis with Diffusion Models