We present MatAtlas, a method for consistent text-guided 3D model texturing.
Following recent progress we leverage a large scale text-to-image generation
model (e.g., Stable Diffusion) as a prior to texture a 3D model. We carefully
design an RGB texturing pipeline that leverages a grid pattern diffusion,
driven by depth and edges. By proposing a multi-step texture refinement
process, we significantly improve the quality and 3D consistency of the
texturing output. To further address the problem of baked-in lighting, we move
beyond RGB colors and pursue assigning parametric materials to the assets.
Given the high-quality initial RGB texture, we propose a novel material
retrieval method capitalized on Large Language Models (LLM), enabling
editabiliy and relightability. We evaluate our method on a wide variety of
geometries and show that our method significantly outperform prior arts. We
also analyze the role of each component through a detailed ablation study.

我们提出了 MatAtlas，一种用于一致的文本引导的 3D 模型纹理方法。通过利用大规模的文本生成模型（例如稳定的扩散器）作为纹理 3D 模型的先验知识。我们设计了一种 RGB 纹理流程，利用了基于深度和边缘的网格模式扩散。通过提出多步骤的纹理细化过程，我们显著提高了纹理输出的质量和 3D 一致性。为了进一步解决烘焙光照的问题，我们超越了 RGB 颜色，将参数化材质分配给资源。鉴于高质量的初始 RGB 纹理，我们提出了一种利用大语言模型（LLM）的新颖材质检索方法，实现了可编辑性和重光能力。我们在各种几何形状上评估了我们的方法，并展示了我们的方法明显优于先前的方法。通过详细的消融研究，我们还分析了每个组件的作用。