mlsanityData to 14 Oct 2026

MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D

arXiv 2411.02336 ↗First posted 4 Nov 2024cs.CVcited by 34

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Abstract (arXiv)

Texturing is a crucial step in the 3D asset production workflow, which enhances the visual appeal and diversity of 3D assets. Despite recent advancements in Text-to-Texture (T2T) generation, existing methods often yield subpar results, primarily due to local discontinuities, inconsistencies across multiple views, and their heavy dependence on UV unwrapping outcomes. To tackle these challenges, we propose a novel generation-refinement 3D texturing framework called MVPaint, which can generate high-resolution, seamless textures while emphasizing multi-view consistency. MVPaint mainly consists of three key modules. 1) Synchronized Multi-view Generation (SMG). Given a 3D mesh model, MVPaint first simultaneously generates multi-view images by employing an SMG model, which leads to coarse texturing results with unpainted parts due to missing observations. 2) Spatial-aware 3D Inpainting (S3I). To ensure complete 3D texturing, we introduce the S3I method, specifically designed to effectively texture previously unobserved areas. 3) UV Refinement (UVR). Furthermore, MVPaint employs a UVR module to improve the texture quality in the UV space, which first performs a UV-space Super-Resolution, followed by a Spatial-aware Seam-Smoothing algorithm for revising spatial texturing discontinuities caused by UV unwrapping. Moreover, we establish two T2T evaluation benchmarks: the Objaverse T2T benchmark and the GSO T2T benchmark, based on selected high-quality 3D meshes from the Objaverse dataset and the entire GSO dataset, respectively. Extensive experimental results demonstrate that MVPaint surpasses existing state-of-the-art methods. Notably, MVPaint could generate high-fidelity textures with minimal Janus issues and highly enhanced cross-view consistency.

Key facts. Cited by 34 papers in the map (data through 14 Oct 2026). Builds on 53 of its 66 references that are in the map. First posted on arXiv 4 Nov 2024.

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53 of 66 references.

References · 53

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38 more
  1. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  2. 2022Objaverse: A Universe of Annotated 3D Objectscited by 1,540
  3. 2023Zero-1-to-3: Zero-shot One Image to 3D Objectcited by 1,489
  4. 2023IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Modelscited by 1,464
  5. 2018Demystifying MMD GANscited by 1,293
  6. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  7. 2023MVDream: Multi-view Diffusion for 3D Generationcited by 938
  8. 2023DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creationcited by 843
  9. 2021CogView: Mastering Text-to-Image Generation via Transformerscited by 694
  10. 2023Wonder3D: Single Image to 3D using Cross-Domain Diffusioncited by 689
  11. 2022Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Itemscited by 612
  12. 2023MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generationcited by 536
  13. 2023Zero123++: a Single Image to Consistent Multi-view Diffusion Base Modelcited by 521
  14. 2022Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priorscited by 487
  15. 2024CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assetscited by 455
  16. 2024CAT3D: Create Anything in 3D with Multi-View Diffusion Modelscited by 432
  17. 2023TEXTure: Text-Guided Texturing of 3D Shapescited by 304
  18. 2022CLIP-Mesh: Generating textured meshes from text using pretrained image-text modelscited by 281
  19. 2023Emu: Enhancing Image Generation Models Using Photogenic Needles in a Haystackcited by 279
  20. 2023ImageDream: Image-Prompt Multi-view Diffusion for 3D Generationcited by 257
  21. 2021CLIP-Forge: Towards Zero-Shot Text-to-Shape Generationcited by 254
  22. 2023Text2Tex: Text-driven Texture Synthesis via Diffusion Modelscited by 235
  23. 2024CogVLM2: Visual Language Models for Image and Video Understandingcited by 217
  24. 2024MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformerscited by 144
  25. 2024Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Imagecited by 135
  26. 2023Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Modelscited by 123
  27. 2023SyncDiffusion: Coherent Montage via Synchronized Joint Diffusionscited by 110
  28. 2024MeshAnything V2: Artist-Created Mesh Generation With Adjacent Mesh Tokenizationcited by 104
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  32. 2023IT3D: Improved Text-to-3D Generation with Explicit View Synthesiscited by 63
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  38. 2024TexGen: Text-Guided 3D Texture Generation with Multi-view Sampling and Resamplingcited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 18; 2026: 16.

34 papers.

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  1. 2025Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generationcited by 356
  2. 2025Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assetscited by 87
  3. 2025TexVerse: A Universe of 3D Objects with High-Resolution Texturescited by 45
  4. 2025UniTEX: Universal High Fidelity Generative Texturing for 3D Shapescited by 23
  5. 2025RomanTex: Decoupling 3D-aware Rotary Positional Embedded Multi-Attention Network for Texture Synthesiscited by 20
  6. 2025MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processingcited by 8
  7. 2025FlexPainter: Flexible and Multi-View Consistent Texture Generationcited by 8
  8. 2025GAP: Gaussianize Any Point Clouds with Text Guidancecited by 7
  9. 2025StyleMe3D: Stylization with Disentangled Priors by Multiple Encoders on 3D Gaussianscited by 6
  10. 2025ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editingcited by 5
  11. 2025LaFiTe: A Generative Latent Field for 3D Native Texturingcited by 4
  12. 2026VecSet-Edit: Unleashing Pre-trained LRM for Mesh Editing from Single Imagecited by 4
22 more
  1. 2025Im2SurfTex: Surface Texture Generation via Neural Backprojection of Multi-View Imagescited by 3
  2. 2025Hi3DEval: Advancing 3D Generation Evaluation with Hierarchical Validitycited by 3
  3. 2025CaliTex: Geometry-Calibrated Attention for View-Coherent 3D Texture Generationcited by 3
  4. 2025PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Modelscited by 2
  5. 2025LumiTex: Towards High-Fidelity PBR Texture Generation with Illumination Contextcited by 2
  6. 2026AssetGen: Deployable 3D Asset Generation at Interactive Speedcited by 2
  7. 2026UMI3D: Robust 3D Generation on Unconstrained Multi-Image Inputs via Simultaneous Focus Cross-Attention Routingcited by 2
  8. 2026Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?cited by 2
  9. 2025CasTex: Cascaded Text-to-Texture Synthesis via Explicit Texture Maps and Physically-Based Shadingcited by 1
  10. 2026MV2UV: Generating High-quality UV Texture Maps with Multiview Promptscited by 1
  11. 2026GaussianGrow: Geometry-aware Gaussian Growing from 3D Point Clouds with Text Guidancecited by 1
  12. 2025CaPa: Carve-n-Paint Synthesis for Efficient 4K Textured Mesh Generationcited by 0
  13. 2026TexSpot: 3D Texture Enhancement with Spatially-uniform Point Latent Representationcited by 0
  14. 2026Towards Realistic 3D Emission Materials: Dataset, Baseline, and Evaluation for Emission Texture Generationcited by 0
  15. 2026Advances in Neural 3D Mesh Texturing: A Surveycited by 0
  16. 2026JanusMesh: Fast and Zero-Shot 3D Visual Illusion Generation via Cross-Space Denoisingcited by 0
  17. 2026Texture++: Elevating 3D Asset Texture Resolution with a Region-Aware Diffusion Modelcited by 0
  18. 2026Learning 3D Editing without Paired Supervision via Generative Prior Distillationcited by 0
  19. 2026PLSR: Progressive and Localized Super-Resolution of 3D Objects via Localized Latent Voxel Diffusioncited by 0
  20. 2026OmniFabric: Coherent UV Space Texture Synthesis for 3D Garment Reconstructioncited by 0
  21. 2026DirectUV: Image-Conditioned UV Texture Generation with Surface-Aware Positional Encodingcited by 0
  22. 2026TaoTex: Boosting Texture Detail Fidelity for Native 3D Material Generationcited by 0