{"ID":2869677,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.13688","arxiv_id":"2509.13688","title":"CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion","abstract":"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.","short_abstract":"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. Ou...","url_abs":"https://arxiv.org/abs/2509.13688","url_pdf":"https://arxiv.org/pdf/2509.13688v3","authors":"[\"James Jincheng\",\"Yuxiao Wu\",\"Youcheng Cai\",\"Ligang Liu\"]","published":"2025-09-17T04:35:48Z","proceeding":"cs.GR","tasks":"[\"cs.GR\",\"cs.AI\"]","methods":"[]","has_code":false}