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CVPR2026Top-tier venue

CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion

James Jincheng Hu, Yuxiao Wu, Youcheng Cai, Ligang Liu

2026Year
3Citations

Abstract

Controllable, high-fidelity mesh editing remains a significant challenge in the domain of 3D content creation. Existing generative methods often struggle with complex geometries and fail to preserve fine-scale details. We propose CraftMesh, a novel framework for high-fidelity generative mesh manipulation based on Poisson Seamless Fusion. Our key insight is to decompose mesh editing into a pipeline that leverages the strengths of 2D image editing and 3D generative modeling: we first edit a 2D reference image, then generate a 3D mesh corresponding to the edited region, and fuse it seamlessly into the original mesh through a Joint Geometry and Appearance Fusion framework built on a hybrid SDF/Mesh representation to enable Poisson Geometry Blending and Poisson Texture Harmonization. Experimental results demonstrate that CraftMesh outperforms state-of-the-art methods, delivering improved structural consistency, richer local geometric and appearance details in challenging editing scenarios. The implementation will be released publicly upon acceptance.

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