View-Consistent 3D Scene Editing via Dual-Path Structural Correspondense and Semantic Continuity

• URL: http://arxiv.org/abs/2604.17801v2
• Date: Fri, 24 Apr 2026 14:52:28 GMT
• Title: View-Consistent 3D Scene Editing via Dual-Path Structural Correspondense and Semantic Continuity
• Abstract summary: Cross-view inconsistency remains a major bottleneck in text-driven 3D scene editing.<n>We recast multi-view consistent 3D editing from a distributional perspective.<n>We introduce a dual-path consistency mechanism consisting of projection-guided structural guidance and patch-level semantic propagation.
• Score: 11.663455227298122
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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