This paper aims to generate materials for 3D meshes from text descriptions. Unlike existing methods that synthesize texture maps, we propose to generate segment-wise procedural material graphs as the appearance representation, which supports high-quality rendering and provides substantial flexibility in editing. Instead of relying on extensive paired data, i.e., 3D meshes with material graphs and corresponding text descriptions, to train a material graph generative model, we propose to leverage the pre-trained 2D diffusion model as a bridge to connect the text and material graphs. Specifically, our approach decomposes a shape into a set of segments and designs a segment-controlled diffusion model to synthesize 2D images that are aligned with mesh parts. Based on generated images, we initialize parameters of material graphs and fine-tune them through the differentiable rendering module to produce materials in accordance with the textual description. Extensive experiments demonstrate the superior performance of our framework in photorealism, resolution, and editability over existing methods. Project page: https://zhanghe3z.github.io/MaPa/

翻译：本文旨在根据文本描述为三维网格生成材质。与现有合成纹理贴图的方法不同，我们提出以逐段程序化材质图作为外观表示形式，该表示支持高质量渲染并在编辑方面具有显著灵活性。我们不依赖大量配对数据（即带有材质图及对应文本描述的三维网格）来训练材质图生成模型，而是利用预训练的二维扩散模型作为连接文本与材质图的桥梁。具体而言，我们的方法将形状分解为若干片段，并设计了一种片段控制扩散模型，用于合成与网格部位对齐的二维图像。基于生成的图像，我们初始化材质图参数，并通过可微分渲染模块对其进行微调，以生成符合文本描述的材质。大量实验表明，本框架在照片真实感、分辨率及可编辑性方面均优于现有方法。项目页面：https://zhanghe3z.github.io/MaPa/