Recently, the surge of efficient and automated 3D AI-generated content (AIGC) methods has increasingly illuminated the path of transforming human imagination into complex 3D structures. However, the automated generation of 3D content is still significantly lags in industrial application. This gap exists because 3D modeling demands high-quality assets with sharp geometry, exquisite topology, and physically based rendering (PBR), among other criteria. To narrow the disparity between generated results and artists' expectations, we introduce GraphicsDreamer, a method for creating highly usable 3D meshes from single images. To better capture the geometry and material details, we integrate the PBR lighting equation into our cross-domain diffusion model, concurrently predicting multi-view color, normal, depth images, and PBR materials. In the geometry fusion stage, we continue to enforce the PBR constraints, ensuring that the generated 3D objects possess reliable texture details, supporting realistic relighting. Furthermore, our method incorporates topology optimization and fast UV unwrapping capabilities, allowing the 3D products to be seamlessly imported into graphics engines. Extensive experiments demonstrate that our model can produce high quality 3D assets in a reasonable time cost compared to previous methods.

翻译：近年来，高效且自动化的三维AI生成内容（AIGC）方法的涌现，为将人类想象转化为复杂三维结构开辟了日益清晰的道路。然而，三维内容的自动化生成在工业应用层面仍存在显著滞后。这一差距源于三维建模对高质量资产的要求，包括锐利的几何形状、精致的拓扑结构以及基于物理的渲染（PBR）等标准。为缩小生成结果与艺术家预期之间的差异，我们提出了GraphicsDreamer——一种从单张图像创建高可用性三维网格的方法。为更好地捕捉几何与材质细节，我们将PBR光照方程集成到跨域扩散模型中，同步预测多视角颜色、法线、深度图像及PBR材质。在几何融合阶段，我们持续强化PBR约束，确保生成的三维物体具备可靠的纹理细节，支持真实感重光照。此外，我们的方法集成了拓扑优化与快速UV展开功能，使得三维产物能够无缝导入图形引擎。大量实验表明，相较于现有方法，我们的模型能够在合理时间成本内生成高质量的三维资产。