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:
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本文旨在通过文本描述生成 3D 网格材料。与现有方法合成纹理图不同，我们提出生成分段逐步建模材料图作为外观表示，支持高质量渲染并提供灵活的编辑。我们的方法通过预训练的 2D 扩散模型作为文本和材料图之间的桥梁，而不是依赖于大量的成对数据训练材料图生成模型。通过在生成的图像上初始化材料图参数，并通过可微分渲染模块进行微调，以符合文本描述。大量实验证明我们的框架在逼真度、分辨率和可编辑性方面的性能优于现有方法。