# Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects

arXiv: https://arxiv.org/abs/2407.02430 · HTML: https://mlsanity.com/paper/2407.02430/

## Key facts

- Cited by 52 papers in the map (data through 14 Oct 2026).
- Builds on 59 of its 66 references that are in the map.
- First posted on arXiv 2 Jul 2024.

## Builds on (most cited first, 30 of 59)

- [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)
- [Hierarchical Text-Conditional Image Generation with CLIP Latents](https://mlsanity.com/paper/2204.06125/) (2022)
- [Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding](https://mlsanity.com/paper/2205.11487/) (2022)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Imagen Video: High Definition Video Generation with Diffusion Models](https://mlsanity.com/paper/2210.02303/) (2022)
- [Make-A-Video: Text-to-Video Generation without Text-Video Data](https://mlsanity.com/paper/2209.14792/) (2022)
- [Zero-1-to-3: Zero-shot One Image to 3D Object](https://mlsanity.com/paper/2303.11328/) (2023)
- [Scaling Autoregressive Models for Content-Rich Text-to-Image Generation](https://mlsanity.com/paper/2206.10789/) (2022)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [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)
- [Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data](https://mlsanity.com/paper/2107.10833/) (2021)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [eDiff-I: Text-to-Image Diffusion Models with an Ensemble of Expert Denoisers](https://mlsanity.com/paper/2211.01324/) (2022)
- [CogView: Mastering Text-to-Image Generation via Transformers](https://mlsanity.com/paper/2105.13290/) (2021)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [Muse: Text-To-Image Generation via Masked Generative Transformers](https://mlsanity.com/paper/2301.00704/) (2023)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation](https://mlsanity.com/paper/2302.08113/) (2023)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [Latent-NeRF for Shape-Guided Generation of 3D Shapes and Textures](https://mlsanity.com/paper/2211.07600/) (2022)
- [Exploiting Diffusion Prior for Real-World Image Super-Resolution](https://mlsanity.com/paper/2305.07015/) (2023)
- [Make-A-Scene: Scene-Based Text-to-Image Generation with Human Priors](https://mlsanity.com/paper/2203.13131/) (2022)
- [Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model](https://mlsanity.com/paper/2311.06214/) (2023)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)

## Built on by (most cited first, 30 of 52)

- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models](https://mlsanity.com/paper/2502.06608/) (2025)
- [MV-Adapter: Multi-view Consistent Image Generation Made Easy](https://mlsanity.com/paper/2412.03632/) (2024)
- [MVPaint: Synchronized Multi-View Diffusion for Painting Anything 3D](https://mlsanity.com/paper/2411.02336/) (2024)
- [TexGaussian: Generating High-quality PBR Material via Octree-based 3D Gaussian Splatting](https://mlsanity.com/paper/2411.19654/) (2024)
- [MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion](https://mlsanity.com/paper/2503.10289/) (2025)
- [UniTEX: Universal High Fidelity Generative Texturing for 3D Shapes](https://mlsanity.com/paper/2505.23253/) (2025)
- [RomanTex: Decoupling 3D-aware Rotary Positional Embedded Multi-Attention Network for Texture Synthesis](https://mlsanity.com/paper/2503.19011/) (2025)
- [Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets](https://mlsanity.com/paper/2510.19944/) (2025)
- [WorldGen: From Text to Traversable and Interactive 3D Worlds](https://mlsanity.com/paper/2511.16825/) (2025)
- [Connecting Consistency Distillation to Score Distillation for Text-to-3D Generation](https://mlsanity.com/paper/2407.13584/) (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)
- [SuperMat: Physically Consistent PBR Material Estimation at Interactive Rates](https://mlsanity.com/paper/2411.17515/) (2024)
- [FlexiTex: Enhancing Texture Generation via Visual Guidance](https://mlsanity.com/paper/2409.12431/) (2024)
- [MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processing](https://mlsanity.com/paper/2503.18461/) (2025)
- [FlexPainter: Flexible and Multi-View Consistent Texture Generation](https://mlsanity.com/paper/2506.02620/) (2025)
- [SeqTex: Generate Mesh Textures in Video Sequence](https://mlsanity.com/paper/2507.04285/) (2025)
- [GAP: Gaussianize Any Point Clouds with Text Guidance](https://mlsanity.com/paper/2508.05631/) (2025)
- [ZeroScene: A Zero-Shot Framework for 3D Scene Generation from a Single Image and Controllable Texture Editing](https://mlsanity.com/paper/2509.23607/) (2025)
- [Jointly Generating Multi-view Consistent PBR Textures using Collaborative Control](https://mlsanity.com/paper/2410.06985/) (2024)
- [LaFiTe: A Generative Latent Field for 3D Native Texturing](https://mlsanity.com/paper/2512.04786/) (2025)
- [Tex4D: Zero-shot 4D Scene Texturing with Video Diffusion Models](https://mlsanity.com/paper/2410.10821/) (2024)
- [SyncSDE: A Probabilistic Framework for Diffusion Synchronization](https://mlsanity.com/paper/2503.21555/) (2025)
- [MatCLIP: Light- and Shape-Insensitive Assignment of PBR Material Models](https://mlsanity.com/paper/2501.15981/) (2025)
- [MD-ProjTex: Texturing 3D Shapes with Multi-Diffusion Projection](https://mlsanity.com/paper/2504.02762/) (2025)
- [PacTure: Efficient PBR Texture Generation on Packed Views with Visual Autoregressive Models](https://mlsanity.com/paper/2505.22394/) (2025)
- [Consistent Zero-shot 3D Texture Synthesis Using Geometry-aware Diffusion and Temporal Video Models](https://mlsanity.com/paper/2506.20946/) (2025)

## Abstract (arXiv)

The recent availability and adaptability of text-to-image models has sparked a new era in many related domains that benefit from the learned text priors as well as high-quality and fast generation capabilities, one of which is texture generation for 3D objects. Although recent texture generation methods achieve impressive results by using text-to-image networks, the combination of global consistency, quality, and speed, which is crucial for advancing texture generation to real-world applications, remains elusive. To that end, we introduce Meta 3D TextureGen: a new feedforward method comprised of two sequential networks aimed at generating high-quality and globally consistent textures for arbitrary geometries of any complexity degree in less than 20 seconds. Our method achieves state-of-the-art results in quality and speed by conditioning a text-to-image model on 3D semantics in 2D space and fusing them into a complete and high-resolution UV texture map, as demonstrated by extensive qualitative and quantitative evaluations. In addition, we introduce a texture enhancement network that is capable of up-scaling any texture by an arbitrary ratio, producing 4k pixel resolution textures.