This project presents DreamEdit3D, a novel framework for personalized 3D scene editing by leveraging multi-view diffusion models. The approach enables users to edit 3D scenes with fine-grained control through personalized text-driven modifications while maintaining multi-view consistency. By personalizing multi-view diffusion models, DreamEdit3D ensures coherent edits across all viewpoints, avoiding the inconsistencies common in single-view editing approaches. The framework enables subject-driven 3D editing by fine-tuning diffusion models on user-provided reference images, allowing precise insertion and modification of objects in 3D scenes. Edited multi-view outputs are combined with 3D Gaussian Splatting for high-quality, real-time renderable 3D scene reconstruction.
Our pipeline consists of three key stages: (1) personalizing a multi-view diffusion model on reference images, (2) generating consistent edited multi-view images via text-driven editing, and (3) reconstructing the final 3D scene using 3D Gaussian Splatting.
Qualitative editing results demonstrating multi-view consistent 3D scene modifications.
Visual comparison with MVEdit, PrEditor3D, and Vox-E.
Demonstrating the diversity and flexibility of our editing framework across various object categories and prompts.
Analyzing the contribution of each component in our pipeline.
@article{ai2026dreamedit3d,
title = {DreamEdit3D: Personalization of Multi-View
Diffusion Models for 3D Editing},
author = {Ai, Jinxin and Nie{\ss}ner, Matthias and Erko\c{c}, Ziya},
journal = {arXiv preprint},
year = {2026}
}
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