# MaterialMVP: Illumination-Invariant Material Generation via Multi-view PBR Diffusion

arXiv: https://arxiv.org/abs/2503.10289 · HTML: https://mlsanity.com/paper/2503.10289/

## Key facts

- Cited by 28 papers in the map (data through 14 Oct 2026).
- Builds on 70 of its 77 references that are in the map.
- First posted on arXiv 13 Mar 2025.

## Builds on (most cited first, 30 of 70)

- [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)
- [Denoising Diffusion Implicit Models](https://mlsanity.com/paper/2010.02502/) (2020)
- [SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis](https://mlsanity.com/paper/2307.01952/) (2023)
- [DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation](https://mlsanity.com/paper/2208.12242/) (2022)
- [DreamFusion: Text-to-3D using 2D Diffusion](https://mlsanity.com/paper/2209.14988/) (2022)
- [Objaverse: A Universe of Annotated 3D Objects](https://mlsanity.com/paper/2212.08051/) (2022)
- [IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models](https://mlsanity.com/paper/2308.06721/) (2023)
- [MVDream: Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2308.16512/) (2023)
- [Objaverse-XL: A Universe of 10M+ 3D Objects](https://mlsanity.com/paper/2307.05663/) (2023)
- [Wonder3D: Single Image to 3D using Cross-Domain Diffusion](https://mlsanity.com/paper/2310.15008/) (2023)
- [Fantasia3D: Disentangling Geometry and Appearance for High-quality Text-to-3D Content Creation](https://mlsanity.com/paper/2303.13873/) (2023)
- [SyncDreamer: Generating Multiview-consistent Images from a Single-view Image](https://mlsanity.com/paper/2309.03453/) (2023)
- [One-2-3-45: Any Single Image to 3D Mesh in 45 Seconds without Per-Shape Optimization](https://mlsanity.com/paper/2306.16928/) (2023)
- [Zero123++: a Single Image to Consistent Multi-view Diffusion Base Model](https://mlsanity.com/paper/2310.15110/) (2023)
- [CLAY: A Controllable Large-scale Generative Model for Creating High-quality 3D Assets](https://mlsanity.com/paper/2406.13897/) (2024)
- [Instant3D: Fast Text-to-3D with Sparse-View Generation and Large Reconstruction Model](https://mlsanity.com/paper/2311.06214/) (2023)
- [Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation](https://mlsanity.com/paper/2501.12202/) (2025)
- [Common Diffusion Noise Schedules and Sample Steps are Flawed](https://mlsanity.com/paper/2305.08891/) (2023)
- [TEXTure: Text-Guided Texturing of 3D Shapes](https://mlsanity.com/paper/2302.01721/) (2023)
- [Hunyuan-DiT: A Powerful Multi-Resolution Diffusion Transformer with Fine-Grained Chinese Understanding](https://mlsanity.com/paper/2405.08748/) (2024)
- [One-2-3-45++: Fast Single Image to 3D Objects with Consistent Multi-View Generation and 3D Diffusion](https://mlsanity.com/paper/2311.07885/) (2023)
- [ImageDream: Image-Prompt Multi-view Diffusion for 3D Generation](https://mlsanity.com/paper/2312.02201/) (2023)
- [Text2Tex: Text-driven Texture Synthesis via Diffusion Models](https://mlsanity.com/paper/2303.11396/) (2023)
- [RGBleftrightarrowX: Image decomposition and synthesis using material- and lighting-aware diffusion models](https://mlsanity.com/paper/2405.00666/) (2024)
- [Unique3D: High-Quality and Efficient 3D Mesh Generation from a Single Image](https://mlsanity.com/paper/2405.20343/) (2024)
- [Paint3D: Paint Anything 3D with Lighting-Less Texture Diffusion Models](https://mlsanity.com/paper/2312.13913/) (2023)
- [Era3D: High-Resolution Multiview Diffusion using Efficient Row-wise Attention](https://mlsanity.com/paper/2405.11616/) (2024)
- [MVDiffusion++: A Dense High-resolution Multi-view Diffusion Model for Single or Sparse-view 3D Object Reconstruction](https://mlsanity.com/paper/2402.12712/) (2024)
- [TexFusion: Synthesizing 3D Textures with Text-Guided Image Diffusion Models](https://mlsanity.com/paper/2310.13772/) (2023)

## Built on by (most cited first, 28 of 28)

- [Hunyuan3D 2.5: Towards High-Fidelity 3D Assets Generation with Ultimate Details](https://mlsanity.com/paper/2506.16504/) (2025)
- [Hunyuan3D-Omni: A Unified Framework for Controllable Generation of 3D Assets](https://mlsanity.com/paper/2509.21245/) (2025)
- [Seed3D 1.0: From Images to High-Fidelity Simulation-Ready 3D Assets](https://mlsanity.com/paper/2510.19944/) (2025)
- [Hunyuan3D Studio: End-to-End AI Pipeline for Game-Ready 3D Asset Generation](https://mlsanity.com/paper/2509.12815/) (2025)
- [Video Perception Models for 3D Scene Synthesis](https://mlsanity.com/paper/2506.20601/) (2025)
- [NaTex: Seamless Texture Generation as Latent Color Diffusion](https://mlsanity.com/paper/2511.16317/) (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)
- [LaFiTe: A Generative Latent Field for 3D Native Texturing](https://mlsanity.com/paper/2512.04786/) (2025)
- [MatPedia: A Universal Generative Foundation for High-Fidelity Material Synthesis](https://mlsanity.com/paper/2511.16957/) (2025)
- [VideoMatGen: PBR Materials through Joint Generative Modeling](https://mlsanity.com/paper/2603.16566/) (2026)
- [ROAR-3D: Routing Arbitrary Views for High-Fidelity 3D Generation](https://mlsanity.com/paper/2605.21121/) (2026)
- [NI-Tex: Non-isometric Image-based Garment Texture Generation](https://mlsanity.com/paper/2511.18765/) (2025)
- [MatMart: Material Reconstruction of 3D Objects via Diffusion](https://mlsanity.com/paper/2511.18900/) (2025)
- [LumiTex: Towards High-Fidelity PBR Texture Generation with Illumination Context](https://mlsanity.com/paper/2511.19437/) (2025)
- [Photo3D: Advancing Photorealistic 3D Generation through Structure-Aligned Detail Enhancement](https://mlsanity.com/paper/2512.08535/) (2025)
- [Hitem3D 2.0: Multi-View Guided Native 3D Texture Generation](https://mlsanity.com/paper/2604.09231/) (2026)
- [Does Native 3D Texture Generation Necessarily Require 3D Assets for Training?](https://mlsanity.com/paper/2609.34621/) (2026)
- [MatLat: Material Latent Space for PBR Texture Generation](https://mlsanity.com/paper/2512.17302/) (2025)
- [MV2UV: Generating High-quality UV Texture Maps with Multiview Prompts](https://mlsanity.com/paper/2603.15436/) (2026)
- [Towards Realistic 3D Emission Materials: Dataset, Baseline, and Evaluation for Emission Texture Generation](https://mlsanity.com/paper/2604.11006/) (2026)
- [Advances in Neural 3D Mesh Texturing: A Survey](https://mlsanity.com/paper/2606.00137/) (2026)
- [Property-Informed Diffusion-Based Text-to-Microstructure Generation](https://mlsanity.com/paper/2606.08150/) (2026)
- [HAFMat: Hybrid Priors Guided Adaptive Fusion for Single-Image Human Material Estimation](https://mlsanity.com/paper/2606.16323/) (2026)
- [RealSkin: Spatio-Spectral Partial Neural Adjoint Maps for Image-to-3D Attribute Transfer](https://mlsanity.com/paper/2607.12495/) (2026)
- [GLOSS: Geometric Local Self-Similarity Learning for Faithful Reference-Guided Texture Fill](https://mlsanity.com/paper/2608.25461/) (2026)
- [Learning 3D Editing without Paired Supervision via Generative Prior Distillation](https://mlsanity.com/paper/2609.04942/) (2026)
- [TaoTex: Boosting Texture Detail Fidelity for Native 3D Material Generation](https://mlsanity.com/paper/2609.34934/) (2026)
- [Texture Space Material Diffusion](https://mlsanity.com/paper/2609.37654/) (2026)

## Abstract (arXiv)

Physically-based rendering (PBR) has become a cornerstone in modern computer graphics, enabling realistic material representation and lighting interactions in 3D scenes. In this paper, we present MaterialMVP, a novel end-to-end model for generating PBR textures from 3D meshes and image prompts, addressing key challenges in multi-view material synthesis. Our approach leverages Reference Attention to extract and encode informative latent from the input reference images, enabling intuitive and controllable texture generation. We also introduce a Consistency-Regularized Training strategy to enforce stability across varying viewpoints and illumination conditions, ensuring illumination-invariant and geometrically consistent results. Additionally, we propose Dual-Channel Material Generation, which separately optimizes albedo and metallic-roughness (MR) textures while maintaining precise spatial alignment with the input images through Multi-Channel Aligned Attention. Learnable material embeddings are further integrated to capture the distinct properties of albedo and MR. Experimental results demonstrate that our model generates PBR textures with realistic behavior across diverse lighting scenarios, outperforming existing methods in both consistency and quality for scalable 3D asset creation.