Controllable, high-fidelity mesh editing remains a significant challenge in 3D content creation. Existing generative methods often struggle with complex geometries and fail to produce detailed results. We propose CraftMesh, a novel framework for high-fidelity generative mesh manipulation via Poisson Seamless Fusion. Our key insight is to decompose mesh editing into a pipeline that leverages the strengths of 2D and 3D generative models: we edit a 2D reference image, then generate a region-specific 3D mesh, and seamlessly fuse it into the original model. We introduce two core techniques: Poisson Geometric Fusion, which utilizes a hybrid SDF/Mesh representation with normal blending to achieve harmonious geometric integration, and Poisson Texture Harmonization for visually consistent texture blending. Experimental results demonstrate that CraftMesh outperforms state-of-the-art methods, delivering superior global consistency and local detail in complex editing tasks.

翻译：可控且高保真的网格编辑仍是3D内容创作中的重大挑战。现有生成方法往往难以处理复杂几何结构，且无法生成精细结果。我们提出CraftMesh，一种通过泊松无缝融合实现高保真生成式网格操作的新颖框架。核心思路是将网格编辑分解为融合2D与3D生成模型优势的流水线：先编辑2D参考图像，再生成区域专属的3D网格，并将其无缝融合至原始模型中。我们引入两项核心技术：泊松几何融合（Poisson Geometric Fusion），利用混合符号距离函数/网格表示结合法向量混合实现和谐的几何整合；泊松纹理协调（Poisson Texture Harmonization）实现视觉一致的纹理混合。实验结果表明，CraftMesh在复杂编辑任务中较现有最优方法取得更优效果，兼具卓越的全局一致性与局部细节。