# Text-Guided Texturing by Synchronized Multi-View Diffusion

arXiv: https://arxiv.org/abs/2311.12891 · HTML: https://mlsanity.com/paper/2311.12891/

## Key facts

- Cited by 87 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 55 references that are in the map.
- First posted on arXiv 21 Nov 2023.

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [High-Resolution Image Synthesis with Latent Diffusion Models](https://mlsanity.com/paper/2112.10752/) (2021)
- [Deep Unsupervised Learning using Nonequilibrium Thermodynamics](https://mlsanity.com/paper/1503.03585/) (2015)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Adding Conditional Control to Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.05543/) (2023)
- [Neural Discrete Representation Learning](https://mlsanity.com/paper/1711.00937/) (2017)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [T2I-Adapter: Learning Adapters to Dig out More Controllable Ability for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2302.08453/) (2023)
- [Magic3D: High-Resolution Text-to-3D Content Creation](https://mlsanity.com/paper/2211.10440/) (2022)
- [ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score Distillation](https://mlsanity.com/paper/2305.16213/) (2023)
- [Point-E: A System for Generating 3D Point Clouds from Complex Prompts](https://mlsanity.com/paper/2212.08751/) (2022)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [Structure and Content-Guided Video Synthesis with Diffusion Models](https://mlsanity.com/paper/2302.03011/) (2023)
- [LION: Latent Point Diffusion Models for 3D Shape Generation](https://mlsanity.com/paper/2210.06978/) (2022)
- [3D Shape Generation and Completion through Point-Voxel Diffusion](https://mlsanity.com/paper/2104.03670/) (2021)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [CLIP-Mesh: Generating textured meshes from text using pretrained image-text models](https://mlsanity.com/paper/2203.13333/) (2022)
- [CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation](https://mlsanity.com/paper/2110.02624/) (2021)
- [Text2Tex: Text-driven Texture Synthesis via Diffusion Models](https://mlsanity.com/paper/2303.11396/) (2023)
- [3DGen: Triplane Latent Diffusion for Textured Mesh Generation](https://mlsanity.com/paper/2303.05371/) (2023)
- [TextMesh: Generation of Realistic 3D Meshes From Text Prompts](https://mlsanity.com/paper/2304.12439/) (2023)
- [Diffusion-SDF: Text-to-Shape via Voxelized Diffusion](https://mlsanity.com/paper/2212.03293/) (2022)
- [TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models](https://mlsanity.com/paper/2310.13772/) (2023)
- [ATT3D: Amortized Text-to-3D Object Synthesis](https://mlsanity.com/paper/2306.07349/) (2023)
- [3D-LDM: Neural Implicit 3D Shape Generation with Latent Diffusion Models](https://mlsanity.com/paper/2212.00842/) (2022)
- [Improved Adversarial Systems for 3D Object Generation and Reconstruction](https://mlsanity.com/paper/1707.09557/) (2017)
- [TextureGAN: Controlling Deep Image Synthesis with Texture Patches](https://mlsanity.com/paper/1706.02823/) (2017)

## Built on by (most cited first, 30 of 87)

- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [Direct3D: Scalable Image-to-3D Generation via 3D Latent Diffusion Transformer](https://mlsanity.com/paper/2405.14832/) (2024)
- [Hunyuan3D 2.1: From Images to High-Fidelity 3D Assets with Production-Ready PBR Material](https://mlsanity.com/paper/2506.15442/) (2025)
- [Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details](https://mlsanity.com/paper/2506.16504/) (2025)
- [MV-Adapter: Multi-view Consistent Image Generation Made Easy](https://mlsanity.com/paper/2412.03632/) (2024)
- [3D Gaussian as a New Era: A Survey](https://mlsanity.com/paper/2402.07181/) (2024)
- [Step1X-3D: Towards High-Fidelity and Controllable Generation of Textured 3D Assets](https://mlsanity.com/paper/2505.07747/) (2025)
- [MeshFormer: High-Quality Mesh Generation with 3D-Guided Reconstruction Model](https://mlsanity.com/paper/2408.10198/) (2024)
- [Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects](https://mlsanity.com/paper/2407.02430/) (2024)
- [DreamMat: High-quality PBR Material Generation with Geometry- and Light-aware Diffusion Models](https://mlsanity.com/paper/2405.17176/) (2024)
- [TEXGen: a Generative Diffusion Model for Mesh Textures](https://mlsanity.com/paper/2411.14740/) (2024)
- [EmbodiedGen: Towards a Generative 3D World Engine for Embodied Intelligence](https://mlsanity.com/paper/2506.10600/) (2025)
- [MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D](https://mlsanity.com/paper/2411.02336/) (2024)
- [Material Anything: Generating Materials for Any 3D Object via Diffusion](https://mlsanity.com/paper/2411.15138/) (2024)
- [MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion](https://mlsanity.com/paper/2503.10289/) (2025)
- [SyncTweedies: A General Generative Framework Based on Synchronized Diffusions](https://mlsanity.com/paper/2403.14370/) (2024)
- [Frankenstein: Generating Semantic-Compositional 3D Scenes in One Tri-Plane](https://mlsanity.com/paper/2403.16210/) (2024)
- [GrounDiT: Grounding Diffusion Transformers via Noisy Patch Transplantation](https://mlsanity.com/paper/2410.20474/) (2024)
- [UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes](https://mlsanity.com/paper/2505.23253/) (2025)
- [MVD2: Efficient Multiview 3D Reconstruction for Multiview Diffusion](https://mlsanity.com/paper/2402.14253/) (2024)
- [RomanTex: Decoupling 3D-aware Rotary Positional Embedded Multi-Attention Network for Texture Synthesis](https://mlsanity.com/paper/2503.19011/) (2025)
- [3D-Adapter: Geometry-Consistent Multi-View Diffusion for High-Quality 3D Generation](https://mlsanity.com/paper/2410.18974/) (2024)
- [Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets](https://mlsanity.com/paper/2510.19944/) (2025)
- [G3PT: Unleash the power of Autoregressive Modeling in 3D Generation via Cross-scale Querying Transformer](https://mlsanity.com/paper/2409.06322/) (2024)
- [TAR3D: Creating High-Quality 3D Assets via Next-Part Prediction](https://mlsanity.com/paper/2412.16919/) (2024)
- [StyleTex: Style Image-Guided Texture Generation for 3D Models](https://mlsanity.com/paper/2411.00399/) (2024)
- [Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds](https://mlsanity.com/paper/2412.07766/) (2024)
- [3DEnhancer: Consistent Multi-View Diffusion for 3D Enhancement](https://mlsanity.com/paper/2412.18565/) (2024)
- [Gen3DEval: Using vLLMs for Automatic Evaluation of Generated 3D Objects](https://mlsanity.com/paper/2504.08125/) (2025)
- [RoomPainter: View-Integrated Diffusion for Consistent Indoor Scene Texturing](https://mlsanity.com/paper/2412.16778/) (2024)

## Abstract (arXiv)

This paper introduces a novel approach to synthesize texture to dress up a given 3D object, given a text prompt. Based on the pretrained text-to-image (T2I) diffusion model, existing methods usually employ a project-and-inpaint approach, in which a view of the given object is first generated and warped to another view for inpainting. But it tends to generate inconsistent texture due to the asynchronous diffusion of multiple views. We believe such asynchronous diffusion and insufficient information sharing among views are the root causes of the inconsistent artifact. In this paper, we propose a synchronized multi-view diffusion approach that allows the diffusion processes from different views to reach a consensus of the generated content early in the process, and hence ensures the texture consistency. To synchronize the diffusion, we share the denoised content among different views in each denoising step, specifically blending the latent content in the texture domain from views with overlap. Our method demonstrates superior performance in generating consistent, seamless, highly detailed textures, comparing to state-of-the-art methods.