DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing
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arXiv
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914572430999552 |
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| author | Ai, Jinxin Nießner, Matthias Erkoç, Ziya |
| author_facet | Ai, Jinxin Nießner, Matthias Erkoç, Ziya |
| contents | While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a significant challenge due to the complexities of multi-view consistency and spatial control. Inspired by these 2D advancements, we present a novel personalization method for text-guided 3D editing that enables compositional, object-level control through natural language. Given a 3D input, we render orthogonal views and extract object-level segmentation masks to isolate semantic components. We then learn distinct token embeddings for each component through a tailored two-phase optimization strategy: multi-view textual inversion with attention alignment, followed by full fine-tuning of multi-view diffusion model. During inference, these disentangled tokens seamlessly compose with editing prompts to generate multi-view consistent images, which are subsequently lifted into high-fidelity textured 3D meshes. Extensive evaluations across diverse editing scenarios demonstrate that our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation compared to existing baselines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_16990 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing Ai, Jinxin Nießner, Matthias Erkoç, Ziya Computer Vision and Pattern Recognition While 2D diffusion models have achieved remarkable success in identity-preserving personalization, extending this capability to 3D assets remains a significant challenge due to the complexities of multi-view consistency and spatial control. Inspired by these 2D advancements, we present a novel personalization method for text-guided 3D editing that enables compositional, object-level control through natural language. Given a 3D input, we render orthogonal views and extract object-level segmentation masks to isolate semantic components. We then learn distinct token embeddings for each component through a tailored two-phase optimization strategy: multi-view textual inversion with attention alignment, followed by full fine-tuning of multi-view diffusion model. During inference, these disentangled tokens seamlessly compose with editing prompts to generate multi-view consistent images, which are subsequently lifted into high-fidelity textured 3D meshes. Extensive evaluations across diverse editing scenarios demonstrate that our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation compared to existing baselines. |
| title | DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2605.16990 |