CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866908918272229376 |
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| author | Jincheng, James Wu, Yuxiao Cai, Youcheng Liu, Ligang |
| author_facet | Jincheng, James Wu, Yuxiao Cai, Youcheng Liu, Ligang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_13688 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion Jincheng, James Wu, Yuxiao Cai, Youcheng Liu, Ligang Graphics Artificial Intelligence 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. |
| title | CraftMesh: High-Fidelity Generative Mesh Manipulation via Poisson Seamless Fusion |
| topic | Graphics Artificial Intelligence |
| url | https://arxiv.org/abs/2509.13688 |