AI RESEARCH
DreamEdit3D: Personalization of Multi-View Diffusion Models for 3D Editing
arXiv CS.CV
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ArXi:2605.16990v1 Announce Type: new 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.