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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2403.11990 |
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| _version_ | 1866916164107501568 |
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| author | Lyu, Zhaoyang Fei, Ben Wang, Jinyi Xu, Xudong Zhang, Ya Yang, Weidong Dai, Bo |
| author_facet | Lyu, Zhaoyang Fei, Ben Wang, Jinyi Xu, Xudong Zhang, Ya Yang, Weidong Dai, Bo |
| contents | Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares. However, due to its irregular structure, mesh creation and manipulation is often time-consuming and labor-intensive. In this paper, we propose a highly controllable generative model, GetMesh, for mesh generation and manipulation across different categories. By taking a varying number of points as the latent representation, and re-organizing them as triplane representation, GetMesh generates meshes with rich and sharp details, outperforming both single-category and multi-category counterparts. Moreover, it also enables fine-grained control over the generation process that previous mesh generative models cannot achieve, where changing global/local mesh topologies, adding/removing mesh parts, and combining mesh parts across categories can be intuitively, efficiently, and robustly accomplished by adjusting the number, positions or features of latent points. Project page is https://getmesh.github.io. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_11990 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GetMesh: A Controllable Model for High-quality Mesh Generation and Manipulation Lyu, Zhaoyang Fei, Ben Wang, Jinyi Xu, Xudong Zhang, Ya Yang, Weidong Dai, Bo Computer Vision and Pattern Recognition Mesh is a fundamental representation of 3D assets in various industrial applications, and is widely supported by professional softwares. However, due to its irregular structure, mesh creation and manipulation is often time-consuming and labor-intensive. In this paper, we propose a highly controllable generative model, GetMesh, for mesh generation and manipulation across different categories. By taking a varying number of points as the latent representation, and re-organizing them as triplane representation, GetMesh generates meshes with rich and sharp details, outperforming both single-category and multi-category counterparts. Moreover, it also enables fine-grained control over the generation process that previous mesh generative models cannot achieve, where changing global/local mesh topologies, adding/removing mesh parts, and combining mesh parts across categories can be intuitively, efficiently, and robustly accomplished by adjusting the number, positions or features of latent points. Project page is https://getmesh.github.io. |
| title | GetMesh: A Controllable Model for High-quality Mesh Generation and Manipulation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2403.11990 |