mlsanityData to 7 Oct 2026

UV-free Texture Generation with Denoising and Geodesic Heat Diffusions

arXiv 2408.16762 ↗First posted 29 Aug 2024cs.CV cs.GR cs.LGcited by 3

Not on the map. Why?

Abstract (arXiv)

Seams, distortions, wasted UV space, vertex-duplication, and varying resolution over the surface are the most prominent issues of the standard UV-based texturing of meshes. These issues are particularly acute when automatic UV-unwrapping techniques are used. For this reason, instead of generating textures in automatically generated UV-planes like most state-of-the-art methods, we propose to represent textures as coloured point-clouds whose colours are generated by a denoising diffusion probabilistic model constrained to operate on the surface of 3D objects. Our sampling and resolution agnostic generative model heavily relies on heat diffusion over the surface of the meshes for spatial communication between points. To enable processing of arbitrarily sampled point-cloud textures and ensure long-distance texture consistency we introduce a fast re-sampling of the mesh spectral properties used during the heat diffusion and introduce a novel heat-diffusion-based self-attention mechanism. Our code and pre-trained models are available at github.com/simofoti/UV3-TeD.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 36 of its 66 references that are in the map. First posted on arXiv 29 Aug 2024.

Builds on

36 of 66 references.

References · 36

  1. 2017Attention Is All You Needcited by 59,932
  2. 2020Denoising Diffusion Probabilistic Modelscited by 20,851
  3. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,604
  4. 2018The Unreasonable Effectiveness of Deep Features as a Perceptual Metriccited by 11,029
  5. 2017PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Spacecited by 4,384
  6. 2014Deep Learning Face Attributes in the Wildcited by 3,679
  7. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  8. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  9. 2018Group Normalizationcited by 1,514
  10. 2018Demystifying MMD GANscited by 1,268
  11. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  12. 2019Quantifying the Carbon Emissions of Machine Learningcited by 516
24 more
  1. 2022Latent-NeRF for Shape-Guided Generation of 3D Shapes and Texturescited by 506
  2. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  3. 2021ABO: Dataset and Benchmarks for Real-World 3D Object Understandingcited by 360
  4. 2022Understanding Diffusion Models: A Unified Perspectivecited by 337
  5. 2023TEXTure: Text-Guided Texturing of 3D Shapescited by 303
  6. 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
  7. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 234
  8. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  9. 2020DiffusionNet: Discretization Agnostic Learning on Surfacescited by 163
  10. 2023Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Modelscited by 122
  11. 2023TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Modelscited by 86
  12. 2022Texturify: Generating Textures on 3D Shape Surfacescited by 65
  13. 2023Texture Generation on 3D Meshes with Point-UV Diffusioncited by 63
  14. 2023On the Design Fundamentals of Diffusion Models: A Surveycited by 59
  15. 20213DStyleNet: Creating 3D Shapes with Geometric and Texture Style Variationscited by 58
  16. 2022AUV-Net: Learning Aligned UV Maps for Texture Transfer and Synthesiscited by 39
  17. 2020SPSG: Self-Supervised Photometric Scene Generation from RGB-D Scanscited by 34
  18. 2023Sin3DM: Learning a Diffusion Model from a Single 3D Textured Shapecited by 29
  19. 2023TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo Supervisioncited by 28
  20. 2023Pointersect: Neural Rendering with Cloud-Ray Intersectioncited by 23
  21. 2021Rendering Point Clouds with Compute Shaders and Vertex Order Optimizationcited by 14
  22. 2023Manifold Diffusion Fieldscited by 11
  23. 2023Single Mesh Diffusion Models with Field Latents for Texture Generationcited by 11
  24. 2023TUVF: Learning Generalizable Texture UV Radiance Fieldscited by 9

Built on by

Cited by year

Citing papers per year: 2025: 3.

3 papers.

  1. 2025Fractional Diffusion Bridge Modelscited by 3
  2. 2025PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Modelscited by 2
  3. 2025DoubleDiffusion: Combining Heat Diffusion with Denoising Diffusion for Texture Generation on 3D Meshescited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2026DirectUV: Image-Conditioned UV Texture Generation with Surface-Aware Positional Encoding
  2. 2024TEXGen: a Generative Diffusion Model for Mesh Textures
  3. 2025NaTex: Seamless Texture Generation as Latent Color Diffusion
  4. 2024An Object is Worth 64x64 Pixels: Generating 3D Object via Image Diffusion
  5. 2024PointDreamer: Zero-shot 3D Textured Mesh Reconstruction from Colored Point Cloud
  6. 2023Consistent Mesh Diffusion
  7. 2023EucliDreamer: Fast and High-Quality Texturing for 3D Models with Stable Diffusion Depth
  8. 2025MeshGen: Generating PBR Textured Mesh with Render-Enhanced Auto-Encoder and Generative Data Augmentation