CMD: Controllable Multiview Diffusion for 3D Editing and Progressive Generation
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arXiv
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| Auteurs principaux: | , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866915373063864320 |
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| author | Li, Peng Ma, Suizhi Chen, Jialiang Liu, Yuan Zhang, Congyi Xue, Wei Luo, Wenhan Sheffer, Alla Wang, Wenping Guo, Yike |
| author_facet | Li, Peng Ma, Suizhi Chen, Jialiang Liu, Yuan Zhang, Congyi Xue, Wei Luo, Wenhan Sheffer, Alla Wang, Wenping Guo, Yike |
| contents | Recently, 3D generation methods have shown their powerful ability to automate 3D model creation. However, most 3D generation methods only rely on an input image or a text prompt to generate a 3D model, which lacks the control of each component of the generated 3D model. Any modifications of the input image lead to an entire regeneration of the 3D models. In this paper, we introduce a new method called CMD that generates a 3D model from an input image while enabling flexible local editing of each component of the 3D model. In CMD, we formulate the 3D generation as a conditional multiview diffusion model, which takes the existing or known parts as conditions and generates the edited or added components. This conditional multiview diffusion model not only allows the generation of 3D models part by part but also enables local editing of 3D models according to the local revision of the input image without changing other 3D parts. Extensive experiments are conducted to demonstrate that CMD decomposes a complex 3D generation task into multiple components, improving the generation quality. Meanwhile, CMD enables efficient and flexible local editing of a 3D model by just editing one rendered image. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_07003 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CMD: Controllable Multiview Diffusion for 3D Editing and Progressive Generation Li, Peng Ma, Suizhi Chen, Jialiang Liu, Yuan Zhang, Congyi Xue, Wei Luo, Wenhan Sheffer, Alla Wang, Wenping Guo, Yike Computer Vision and Pattern Recognition Recently, 3D generation methods have shown their powerful ability to automate 3D model creation. However, most 3D generation methods only rely on an input image or a text prompt to generate a 3D model, which lacks the control of each component of the generated 3D model. Any modifications of the input image lead to an entire regeneration of the 3D models. In this paper, we introduce a new method called CMD that generates a 3D model from an input image while enabling flexible local editing of each component of the 3D model. In CMD, we formulate the 3D generation as a conditional multiview diffusion model, which takes the existing or known parts as conditions and generates the edited or added components. This conditional multiview diffusion model not only allows the generation of 3D models part by part but also enables local editing of 3D models according to the local revision of the input image without changing other 3D parts. Extensive experiments are conducted to demonstrate that CMD decomposes a complex 3D generation task into multiple components, improving the generation quality. Meanwhile, CMD enables efficient and flexible local editing of a 3D model by just editing one rendered image. |
| title | CMD: Controllable Multiview Diffusion for 3D Editing and Progressive Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.07003 |