Learning radiance fields (NeRF) with powerful 2D diffusion models has garnered popularity for text-to-3D generation. Nevertheless, the implicit 3D representations of NeRF lack explicit modeling of meshes and textures over surfaces, and such surface-undefined way may suffer from the issues, e.g., noisy surfaces with ambiguous texture details or cross-view inconsistency. To alleviate this, we present DreamMesh, a novel text-to-3D architecture that pivots on well-defined surfaces (triangle meshes) to generate high-fidelity explicit 3D model. Technically, DreamMesh capitalizes on a distinctive coarse-to-fine scheme. In the coarse stage, the mesh is first deformed by text-guided Jacobians and then DreamMesh textures the mesh with an interlaced use of 2D diffusion models in a tuning free manner from multiple viewpoints. In the fine stage, DreamMesh jointly manipulates the mesh and refines the texture map, leading to high-quality triangle meshes with high-fidelity textured materials. Extensive experiments demonstrate that DreamMesh significantly outperforms state-of-the-art text-to-3D methods in faithfully generating 3D content with richer textual details and enhanced geometry. Our project page is available at https://dreammesh.github.io.

翻译：利用强大的二维扩散模型学习辐射场（NeRF）已成为文本到三维生成的主流方法。然而，NeRF的隐式三维表示缺乏对表面网格与纹理的显式建模，这种表面未定义的方式可能导致噪声表面、纹理细节模糊或跨视角不一致等问题。为缓解此问题，我们提出DreamMesh——一种基于明确定义的表面（三角网格）来生成高保真显式三维模型的新型文本到三维架构。技术上，DreamMesh采用独特的由粗到精策略：在粗粒度阶段，首先通过文本引导的雅可比矩阵对网格进行形变，随后DreamMesh以免调参方式从多视角交错运用二维扩散模型为网格生成纹理；在精细阶段，DreamMesh同步优化网格形变并精修纹理贴图，最终生成具有高保真纹理材质的高质量三角网格。大量实验表明，DreamMesh在忠实生成富含文本细节且几何增强的三维内容方面显著优于现有文本到三维方法。项目页面详见 https://dreammesh.github.io。