Text-driven 3D scene editing has recently attracted increasing attention. Most existing methods follow a render-edit-optimize pipeline, where multi-view images are rendered from a 3D scene, edited with 2D image editors, and then used to optimize the underlying 3D representation. However, cross-view inconsistency remains a major bottleneck. Although recent methods introduce geometric cues, cross-view interactions, or video priors to mitigate this issue, they still largely rely on inference-time synchronization and thus remain limited in robustness and generalization.In this work, we recast multi-view consistent 3D editing from a distributional perspective: 3D scene editing essentially requires a joint distribution modeling across viewpoints.Based on this insight, we propose a view-consistent 3D editing framework that explicitly introduces cross-view dependencies into the editing process. Furthermore, motivated by the observation that structural correspondence and semantic continuity rely on different cross-view cues, we introduce a dual-path consistency mechanism consisting of projection-guided structural guidance and patch-level semantic propagation for effective cross-view editing. Further, we construct a paired multi-view editing dataset that provides reliable supervision for learning cross-view consistency in edited scenes. Extensive experiments demonstrate that our method achieves superior editing performance with precise and consistent views for complex scenes.

翻译：文本驱动的三维场景编辑近年来备受关注。现有方法大多遵循"渲染-编辑-优化"流程，即从三维场景渲染多视角图像，经二维图像编辑器处理后，再用于优化底层三维表征。然而，跨视角不一致性仍是主要瓶颈。尽管近期研究引入几何线索、跨视角交互或视频先验来缓解该问题，但这些方法仍高度依赖推理阶段的同步机制，鲁棒性与泛化能力有限。本文从分布视角重新定义多视角一致的三维编辑问题：三维场景编辑本质要求建立跨视角的联合分布建模。基于这一见解，我们提出一种视图一致的三维编辑框架，将跨视角依赖显式融入编辑过程。此外，受结构对应与语义连续性依赖不同跨视角线索这一现象启发，我们引入双路径一致性机制，包含投影引导的结构引导与补丁级语义传播，以实现高效跨视角编辑。进一步地，我们构建了成对的多视角编辑数据集，为学习编辑场景中的跨视角一致性提供可靠监督。大量实验表明，本方法在复杂场景中可实现精准且保持视图一致的卓越编辑效果。