用2D图像编辑+3D网格融合，实现高保真三维模型修改
CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion
- 先改2D参考图，再生成局部3D网格，最后无缝融合进原模型
- 在复杂编辑任务中保持全局一致性与细节精度，优于现有方法
- 适合需要精细控制的3D建模、游戏道具设计等场景
可控且高保真的网格编辑在三维内容创作中仍具挑战。现有生成方法常因复杂几何结构而难以生成细节丰富的结果。我们提出CraftMesh，一种基于泊松无痕融合的高保真生成式网格操作框架。核心思想是将网格编辑分解为利用2D与3D生成模型优势的流程：先编辑2D参考图像，再生成区域特定的3D网格，并无缝融合至原始模型。我们引入两项核心技术：泊松几何融合（Poisson Geometric Fusion），采用混合SDF/网格表示与法向融合实现和谐几何整合；以及泊松纹理调和（Poisson Texture Harmonization），实现视觉一致的纹理融合。实验表明，CraftMesh在复杂编辑任务中显著优于当前最优方法，展现出更优的全局一致性和局部细节表现。
原文摘要 · Abstract (English)
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.
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