DisCo3D: Distilling Multi-View Consistency for 3D Scene Editing

• URL: http://arxiv.org/abs/2508.01684v1
• Date: Sun, 03 Aug 2025 09:27:41 GMT
• Title: DisCo3D: Distilling Multi-View Consistency for 3D Scene Editing
• Authors: Yufeng Chi, Huimin Ma, Kafeng Wang, Jianmin Li,
• Abstract summary: We propose textbfDisCo3D, a novel framework that distills 3D consistency priors into a 2D editor.<n>Our method first fine-tunes a 3D generator using multi-view inputs for scene adaptation, then trains a 2D editor through consistency distillation.<n> Experimental results show DisCo3D achieves stable multi-view consistency and outperforms state-of-the-art methods in editing quality.
• Score: 12.383291424229448
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While diffusion models have demonstrated remarkable progress in 2D image generation and editing, extending these capabilities to 3D editing remains challenging, particularly in maintaining multi-view consistency. Classical approaches typically update 3D representations through iterative refinement based on a single editing view. However, these methods often suffer from slow convergence and blurry artifacts caused by cross-view inconsistencies. Recent methods improve efficiency by propagating 2D editing attention features, yet still exhibit fine-grained inconsistencies and failure modes in complex scenes due to insufficient constraints. To address this, we propose \textbf{DisCo3D}, a novel framework that distills 3D consistency priors into a 2D editor. Our method first fine-tunes a 3D generator using multi-view inputs for scene adaptation, then trains a 2D editor through consistency distillation. The edited multi-view outputs are finally optimized into 3D representations via Gaussian Splatting. Experimental results show DisCo3D achieves stable multi-view consistency and outperforms state-of-the-art methods in editing quality.

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