用多视角生成与智能后处理，实现更逼真的3D材质贴图。
MuMA: 3D PBR Texturing via Multi-Channel Multi-View Generation and Agentic Post-Processing
- 分通道生成阴影与漫反射图像，融合材质分解模块。
- 视觉质量与材质保真度优于现有方法。
- 适合需要高质量3D纹理的设计师和游戏开发者。
当前3D生成在物理基础渲染（PBR）贴图方面仍存在不足，主要受限于数据稀缺及多通道材质建模难题。本文提出MuMA，通过多通道多视角生成与智能后处理实现3D PBR贴图。方法核心创新包括：1）建模阴影与漫反射外观通道，引入内在分解模块以捕捉材质属性；2）利用多模态大语言模型模拟艺术家的材质评估与选择流程。实验表明，相较于现有方法，MuMA在视觉质量和材质保真度上均有显著提升。
原文摘要 · Abstract (English)
Current methods for 3D generation still fall short in physically based rendering (PBR) texturing, primarily due to limited data and challenges in modeling multi-channel materials. In this work, we propose MuMA, a method for 3D PBR texturing through Multi-channel Multi-view generation and Agentic post-processing. Our approach features two key innovations: 1) We opt to model shaded and albedo appearance channels, where the shaded channels enables the integration intrinsic decomposition modules for material properties. 2) Leveraging multimodal large language models, we emulate artists' techniques for material assessment and selection. Experiments demonstrate that MuMA achieves superior results in visual quality and material fidelity compared to existing methods.
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