mlsanityData to 7 Oct 2026

TUVF: Learning Generalizable Texture UV Radiance Fields

arXiv 2305.03040 ↗First posted 4 May 2023cs.CVcited by 9

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Abstract (arXiv)

Textures are a vital aspect of creating visually appealing and realistic 3D models. In this paper, we study the problem of generating high-fidelity texture given shapes of 3D assets, which has been relatively less explored compared with generic 3D shape modeling. Our goal is to facilitate a controllable texture generation process, such that one texture code can correspond to a particular appearance style independent of any input shapes from a category. We introduce Texture UV Radiance Fields (TUVF) that generate textures in a learnable UV sphere space rather than directly on the 3D shape. This allows the texture to be disentangled from the underlying shape and transferable to other shapes that share the same UV space, i.e., from the same category. We integrate the UV sphere space with the radiance field, which provides a more efficient and accurate representation of textures than traditional texture maps. We perform our experiments on synthetic and real-world object datasets where we achieve not only realistic synthesis but also substantial improvements over state-of-the-arts on texture controlling and editing. Project Page: https://www.anjiecheng.me/TUVF

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 69 of its 104 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

69 of 104 references.

References · 69

  1. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,694
  2. 2023Segment Anythingcited by 8,158
  3. 2018A Style-Based Generator Architecture for Generative Adversarial Networkscited by 5,986
  4. 2022Generative Adversarial Networkscited by 5,603
  5. 2023Adding Conditional Control to Text-to-Image Diffusion Modelscited by 5,466
  6. 2013Auto-Encoding Variational Bayescited by 3,980
  7. 2019Analyzing and Improving the Image Quality of StyleGANcited by 3,580
  8. 2015ShapeNet: An Information-Rich 3D Model Repositorycited by 2,880
  9. 2018Dynamic Graph CNN for Learning on Point Cloudscited by 2,220
  10. 2021Mip-NeRF: A Multiscale Representation for Anti-Aliasing Neural Radiance Fieldscited by 1,957
  11. 2021NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstructioncited by 1,701
  12. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,574
38 more
  1. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  2. 2020NeRF in the Wild: Neural Radiance Fields for Unconstrained Photo Collectionscited by 1,230
  3. 2021Efficient Geometry-aware 3D Generative Adversarial Networkscited by 1,204
  4. 2020Training Generative Adversarial Networks with Limited Datacited by 1,184
  5. 2021Volume Rendering of Neural Implicit Surfacescited by 990
  6. 2016InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Netscited by 858
  7. 2018Self-Attention Generative Adversarial Networkscited by 809
  8. 2018Multimodal Unsupervised Image-to-Image Translationcited by 795
  9. 2014Deep Learning in Neural Networks: An Overviewcited by 765
  10. 2021MVSNeRF: Fast Generalizable Radiance Field Reconstruction from Multi-View Stereocited by 735
  11. 2020pi-GAN: Periodic Implicit Generative Adversarial Networks for 3D-Aware Image Synthesiscited by 708
  12. 2019Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervisioncited by 699
  13. 2021IBRNet: Learning Multi-View Image-Based Renderingcited by 698
  14. 2020GRAF: Generative Radiance Fields for 3D-Aware Image Synthesiscited by 688
  15. 2023Zip-NeRF: Anti-Aliased Grid-Based Neural Radiance Fieldscited by 667
  16. 2021UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstructioncited by 643
  17. 2021Ref-NeRF: Structured View-Dependent Appearance for Neural Radiance Fieldscited by 569
  18. 2022LION: Latent Point Diffusion Models for 3D Shape Generationcited by 552
  19. 2022Point-NeRF: Point-based Neural Radiance Fieldscited by 503
  20. 2022GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Imagescited by 446
  21. 2019Soft Rasterizer: A Differentiable Renderer for Image-based 3D Reasoningcited by 420
  22. 2020Deep Local Shapes: Learning Local SDF Priors for Detailed 3D Reconstructioncited by 321
  23. 2021Neural RGB-D Surface Reconstructioncited by 312
  24. 20163D Menagerie: Modeling the 3D shape and pose of animalscited by 310
  25. 2022SDFusion: Multimodal 3D Shape Completion, Reconstruction, and Generationcited by 287
  26. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 234
  27. 2016Adversarially Learned Inferencecited by 216
  28. 2020Swapping Autoencoder for Deep Image Manipulationcited by 215
  29. 2019Texture Fields: Learning Texture Representations in Function Spacecited by 210
  30. 2021Shape As Points: A Differentiable Poisson Solvercited by 186
  31. 2020GRF: Learning a General Radiance Field for 3D Representation and Renderingcited by 183
  32. 2020Adversarial Latent Autoencoderscited by 155
  33. 2022NeuMesh: Learning Disentangled Neural Mesh-based Implicit Field for Geometry and Texture Editingcited by 141
  34. 2021NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wildcited by 136
  35. 2021MINE: Towards Continuous Depth MPI with NeRF for Novel View Synthesiscited by 133
  36. 2015A Large-Scale Car Dataset for Fine-Grained Categorization and Verificationcited by 129
  37. 2015Deep Convolutional Inverse Graphics Networkcited by 121
  38. 2022Is Attention All That NeRF Needs?cited by 113

50 most cited of 69 All 69 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 2; 2024: 3; 2025: 2; 2026: 2.

9 papers.

  1. 2023Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Modelscited by 122
  2. 2024TEXGen: a Generative Diffusion Model for Mesh Texturescited by 46
  3. 2023Single Mesh Diffusion Models with Field Latents for Texture Generationcited by 11
  4. 2025FlexPainter: Flexible and Multi-View Consistent Texture Generationcited by 7
  5. 2025SeqTex: Generate Mesh Textures in Video Sequencecited by 7
  6. 2024Tactile DreamFusion: Exploiting Tactile Sensing for 3D Generationcited by 6
  7. 2024UV-free Texture Generation with Denoising and Geodesic Heat Diffusionscited by 3
  8. 2026Restore3D: Breathing Life into Broken Objects with Shape and Texture Restorationcited by 0
  9. 2026GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fillcited by 0