ConcordFlow: View-Aware Complex 3D Editing via Flow Aggregation

Existing image-guided 3D editing methods provide effective control when a requested change can be clearly specified from a single view. Complex instructions, however, may involve multiple changes best observed from different viewpoints. Applying these changes sequentially through multi-round editing can lead to error accumulation, while jointly processing multiple view-specific conditions requires resolving spatially varying editing effects and potential conflicts among updates. To address these challenges, we introduce ConcordFlow, a mask-free and training-free framework built on TRELLIS for view-aware complex 3D editing that jointly integrates multiple conditions in a single 3D editing process. In the sparse-structure stage, trajectory-guided spatial weighting (TGSW) derives spatial weights from independent editing and restoration trajectories to identify each condition's editing influence. Disagreement-guided reweighting (DGR) then adjusts these weights during joint editing using source–target velocity disagreement. In the subsequent structured-latent stage, disagreement–visibility adaptive fusion (DVAF) likewise compares source- and target-conditioned velocities and combines the resulting disagreement with geometric visibility to guide feature generation. To evaluate our framework, we introduce ComplexEdit3D-Bench for view-aware complex 3D editing. On this benchmark, ConcordFlow achieves stronger condition alignment and better overall source preservation than the compared multi-round methods.