Physically Based Rendering (PBR) materials play a crucial role in modern graphics, enabling photorealistic rendering across diverse environment maps. Developing an effective and efficient algorithm that is capable of automatically generating high-quality PBR materials rather than RGB texture for 3D meshes can significantly streamline the 3D content creation. Most existing methods leverage pre-trained 2D diffusion models for multi-view image synthesis, which often leads to severe inconsistency between the generated textures and input 3D meshes. This paper presents TexGaussian, a novel method that uses octant-aligned 3D Gaussian Splatting for rapid PBR material generation. Specifically, we place each 3D Gaussian on the finest leaf node of the octree built from the input 3D mesh to render the multi-view images not only for the albedo map but also for roughness and metallic. Moreover, our model is trained in a regression manner instead of diffusion denoising, capable of generating the PBR material for a 3D mesh in a single feed-forward process. Extensive experiments on publicly available benchmarks demonstrate that our method synthesizes more visually pleasing PBR materials and runs faster than previous methods in both unconditional and text-conditional scenarios, exhibiting better consistency with the given geometry. Our code and trained models are available at https://3d-aigc.github.io/TexGaussian.

翻译：基于物理的渲染（PBR）材质在现代图形学中具有关键作用，能够在多样化环境贴图中实现照片级真实感渲染。开发一种高效且能自动为三维网格生成高质量PBR材质（而非RGB纹理）的算法，可显著简化三维内容创作流程。现有方法大多利用预训练的二维扩散模型进行多视角图像合成，这常导致生成纹理与输入三维网格间存在严重不一致性。本文提出TexGaussian——一种基于八叉树对齐三维高斯泼溅的快速PBR材质生成新方法。具体而言，我们将每个三维高斯置于输入三维网格构建的八叉树最精细叶节点上，从而渲染出适用于反照率贴图、粗糙度与金属度的多视角图像。此外，本模型采用回归式训练而非扩散去噪机制，可通过单次前向传播为三维网格生成PBR材质。在公开基准测试上的大量实验表明，本方法在无条件与文本条件场景下均能合成视觉质量更优的PBR材质，且运行速度优于现有方法，同时与给定几何结构保持更好的一致性。代码与训练模型已发布于 https://3d-aigc.github.io/TexGaussian。