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

arXiv:2508.01684v2 Announce Type: replace 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 extbf{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.

Related

• ViewMask-1-to-3: Multi-View Consistent Image Generation via Multimodal Discrete Diffusion Models
• I3DM: Implicit 3D-aware Memory Retrieval and Injection for Consistent Video Scene Generation
• BoostDream: Efficient Refining for High-Quality Text-to-3D Generation from Multi-View Diffusion
• WorldMesh: Generating Navigable Multi-Room 3D Scenes via Mesh-Conditioned Image Diffusion
• UniGeo: Unifying Geometric Guidance for Camera-Controllable Image Editing via Video Models

Source: arXiv cs.CV | 2026-09-01