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 multiview 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, which exhibit better consistency with the given geometry. Our code and trained models are available at this https URL.
基于物理渲染（Physically Based Rendering, PBR） 材质在现代图形学中具有关键作用，使得在多种环境光照条件下实现真实感渲染成为可能。开发一种能够自动为 3D 网格生成高质量 PBR 材质（而非仅 RGB 纹理）的高效算法，可以显著简化 3D 内容创作流程。然而，现有大多数方法利用预训练的 2D 扩散模型进行多视图图像合成，常导致生成的纹理与输入 3D 网格之间严重不一致。 为此，我们提出了 TexGaussian，一种利用八分体对齐的 3D Gaussian Splatting 快速生成 PBR 材质的新方法。具体来说，我们将每个 3D 高斯点置于从输入 3D 网格构建的八叉树的最细叶节点上，以渲染多视图图像，不仅生成反照率（albedo）图，还包括粗糙度（roughness）和金属性（metallic）图。此外，我们的模型通过回归方式训练，而非扩散去噪，这使得在一次前向传播过程中即可完成 3D 网格的 PBR 材质生成。 在公开基准数据集上的广泛实验表明，TexGaussian 在无条件和文本条件场景下均能合成更具视觉吸引力的 PBR 材质，同时运行速度显著快于现有方法，并与给定几何保持更好的一致性。这使得 TexGaussian 成为高效生成高质量 PBR 材质的有力工具，为 3D 内容创作带来新突破。