# Mesh2Tex: Generating Mesh Textures from Image Queries

arXiv: https://arxiv.org/abs/2304.05868 · HTML: https://mlsanity.com/paper/2304.05868/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 44 of its 52 references that are in the map.
- First posted on arXiv 12 Apr 2023.

## Builds on (most cited first, 30 of 44)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [The Unreasonable Effectiveness of Deep Features as a Perceptual Metric](https://mlsanity.com/paper/1801.03924/) (2018)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes](https://mlsanity.com/paper/1702.04405/) (2017)
- [ShapeNet: An Information-Rich 3D Model Repository](https://mlsanity.com/paper/1512.03012/) (2015)
- [DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation](https://mlsanity.com/paper/1901.05103/) (2019)
- [Occupancy Networks: Learning 3D Reconstruction in Function Space](https://mlsanity.com/paper/1812.03828/) (2018)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Efficient Geometry-aware 3D Generative Adversarial Networks](https://mlsanity.com/paper/2112.07945/) (2021)
- [Learning Implicit Fields for Generative Shape Modeling](https://mlsanity.com/paper/1812.02822/) (2018)
- [PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitization](https://mlsanity.com/paper/1905.05172/) (2019)
- [Accelerating 3D Deep Learning with PyTorch3D](https://mlsanity.com/paper/2007.08501/) (2020)
- [Diffusion Probabilistic Models for 3D Point Cloud Generation](https://mlsanity.com/paper/2103.01458/) (2021)
- [PIFuHD: Multi-Level Pixel-Aligned Implicit Function for High-Resolution 3D Human Digitization](https://mlsanity.com/paper/2004.00452/) (2020)
- [Zero-Shot Text-Guided Object Generation with Dream Fields](https://mlsanity.com/paper/2112.01455/) (2021)
- [Point-NeRF: Point-based Neural Radiance Fields](https://mlsanity.com/paper/2201.08845/) (2022)
- [Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation](https://mlsanity.com/paper/1901.02970/) (2019)
- [GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images](https://mlsanity.com/paper/2209.11163/) (2022)
- [CLIP-NeRF: Text-and-Image Driven Manipulation of Neural Radiance Fields](https://mlsanity.com/paper/2112.05139/) (2021)
- [Text2Mesh: Text-Driven Neural Stylization for Meshes](https://mlsanity.com/paper/2112.03221/) (2021)
- [DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction](https://mlsanity.com/paper/1905.10711/) (2019)
- [ConceptFusion: Open-set Multimodal 3D Mapping](https://mlsanity.com/paper/2302.07241/) (2023)
- [PolyGen: An Autoregressive Generative Model of 3D Meshes](https://mlsanity.com/paper/2002.10880/) (2020)
- [CLIP-Forge: Towards Zero-Shot Text-to-Shape Generation](https://mlsanity.com/paper/2110.02624/) (2021)
- [Language-Grounded Indoor 3D Semantic Segmentation in the Wild](https://mlsanity.com/paper/2204.07761/) (2022)
- [Texture Fields: Learning Texture Representations in Function Space](https://mlsanity.com/paper/1905.07259/) (2019)

## Built on by (most cited first, 30 of 31)

- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models](https://mlsanity.com/paper/2312.13913/) (2023)
- [FlashTex: Fast Relightable Mesh Texturing with LightControlNet](https://mlsanity.com/paper/2402.13251/) (2024)
- [TextureDreamer: Image-guided Texture Synthesis through Geometry-aware Diffusion](https://mlsanity.com/paper/2401.09416/) (2024)
- [Meta 3D TextureGen: Fast and Consistent Texture Generation for 3D Objects](https://mlsanity.com/paper/2407.02430/) (2024)
- [TEXGen: a Generative Diffusion Model for Mesh Textures](https://mlsanity.com/paper/2411.14740/) (2024)
- [Single-view 3D Scene Reconstruction with High-fidelity Shape and Texture](https://mlsanity.com/paper/2311.00457/) (2023)
- [3D Paintbrush: Local Stylization of 3D Shapes with Cascaded Score Distillation](https://mlsanity.com/paper/2311.09571/) (2023)
- [TexPainter: Generative Mesh Texturing with Multi-view Consistency](https://mlsanity.com/paper/2406.18539/) (2024)
- [Garment3DGen: 3D Garment Stylization and Texture Generation](https://mlsanity.com/paper/2403.18816/) (2024)
- [AI-Generated Content (AIGC) for Various Data Modalities: A Survey](https://mlsanity.com/paper/2308.14177/) (2023)
- [EASI-Tex: Edge-Aware Mesh Texturing from Single Image](https://mlsanity.com/paper/2405.17393/) (2024)
- [RoomTex: Texturing Compositional Indoor Scenes via Iterative Inpainting](https://mlsanity.com/paper/2406.02461/) (2024)
- [Make-A-Texture: Fast Shape-Aware Texture Generation in 3 Seconds](https://mlsanity.com/paper/2412.07766/) (2024)
- [Mesh Neural Cellular Automata](https://mlsanity.com/paper/2311.02820/) (2023)
- [Single Mesh Diffusion Models with Field Latents for Texture Generation](https://mlsanity.com/paper/2312.09250/) (2023)
- [FlexPainter: Flexible and Multi-View Consistent Texture Generation](https://mlsanity.com/paper/2506.02620/) (2025)
- [UrbanCAD: Towards Highly Controllable and Photorealistic 3D Vehicles for Urban Scene Simulation](https://mlsanity.com/paper/2411.19292/) (2024)
- [SeqTex: Generate Mesh Textures in Video Sequence](https://mlsanity.com/paper/2507.04285/) (2025)
- [ChromaDistill: Colorizing Monochrome Radiance Fields with Knowledge Distillation](https://mlsanity.com/paper/2309.07668/) (2023)
- [DragTex: Generative Point-Based Texture Editing on 3D Mesh](https://mlsanity.com/paper/2403.02217/) (2024)
- [Meta 3D Gen](https://mlsanity.com/paper/2407.02599/) (2024)
- [3D PixBrush: Image-Guided Local Texture Synthesis](https://mlsanity.com/paper/2507.03731/) (2025)
- [TriTex: Learning Texture from a Single Mesh via Triplane Semantic Features](https://mlsanity.com/paper/2503.16630/) (2025)
- [GuideFlow3D: Optimization-Guided Rectified Flow For Appearance Transfer](https://mlsanity.com/paper/2510.16136/) (2025)
- [GOATex: Geometry & Occlusion-Aware Texturing](https://mlsanity.com/paper/2511.23051/) (2025)
- [CustomTex: High-fidelity Indoor Scene Texturing via Multi-Reference Customization](https://mlsanity.com/paper/2603.19121/) (2026)
- [GarmentPainter: Efficient 3D Garment Texture Synthesis with Character-Guided Diffusion Model](https://mlsanity.com/paper/2603.08228/) (2026)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [Restore3D: Breathing Life into Broken Objects with Shape and Texture Restoration](https://mlsanity.com/paper/2607.00522/) (2026)

## Abstract (arXiv)

Remarkable advances have been achieved recently in learning neural representations that characterize object geometry, while generating textured objects suitable for downstream applications and 3D rendering remains at an early stage. In particular, reconstructing textured geometry from images of real objects is a significant challenge -- reconstructed geometry is often inexact, making realistic texturing a significant challenge. We present Mesh2Tex, which learns a realistic object texture manifold from uncorrelated collections of 3D object geometry and photorealistic RGB images, by leveraging a hybrid mesh-neural-field texture representation. Our texture representation enables compact encoding of high-resolution textures as a neural field in the barycentric coordinate system of the mesh faces. The learned texture manifold enables effective navigation to generate an object texture for a given 3D object geometry that matches to an input RGB image, which maintains robustness even under challenging real-world scenarios where the mesh geometry approximates an inexact match to the underlying geometry in the RGB image. Mesh2Tex can effectively generate realistic object textures for an object mesh to match real images observations towards digitization of real environments, significantly improving over previous state of the art.