DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing

• URL: http://arxiv.org/abs/2605.16990v1
• Date: Sat, 16 May 2026 13:21:22 GMT
• Title: DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing
• Abstract summary: We present a novel personalization method for text-guided 3D editing that enables compositional, object-level control through natural language.<n>Our method successfully transfers the flexibility of 2D personalization to 3D, achieving state-of-the-art edit faithfulness and identity preservation.
• Score: 47.437239634403426
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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