CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion
James Jincheng Hu, Yuxiao Wu, Youcheng Cai, Ligang Liu
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. We decompose mesh editing into a pipeline that leverages the strengths of 2D image editing and 3D generation models: 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 Geometry and Texture Fusion method. 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. We demonstrate state-of-the-art structural consistency, geometric fidelity, and texture quality in challenging editing scenarios.
BibTeX
@inproceedings{cvpr2026_craftmeshhighfid,
title = {CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion},
author = {James Jincheng Hu and Yuxiao Wu and Youcheng Cai and Ligang Liu},
booktitle = {CVPR 2026},
year = {2026}
}