FluSplat: Sparse-View 3D Editing without Test-Time Optimization

• URL: http://arxiv.org/abs/2604.20038v1
• Date: Tue, 21 Apr 2026 22:45:20 GMT
• Title: FluSplat: Sparse-View 3D Editing without Test-Time Optimization
• Abstract summary: Existing pipelines rely on iterative edit-and-fit optimization at test time.<n>We propose a feed-forward framework for cross-view consistent 3D scene editing from sparse views.<n> Experiments demonstrate competitive editing fidelity and substantially improved cross-view consistency.
• Score: 14.837124413289883
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in text-guided image editing and 3D Gaussian Splatting (3DGS) have enabled high-quality 3D scene manipulation. However, existing pipelines rely on iterative edit-and-fit optimization at test time, alternating between 2D diffusion editing and 3D reconstruction. This process is computationally expensive, scene-specific, and prone to cross-view inconsistencies. We propose a feed-forward framework for cross-view consistent 3D scene editing from sparse views. Instead of enforcing consistency through iterative 3D refinement, we introduce a cross-view regularization scheme in the image domain during training. By jointly supervising multi-view edits with geometric alignment constraints, our model produces view-consistent results without per-scene optimization at inference. The edited views are then lifted into 3D via a feedforward 3DGS model, yielding a coherent 3DGS representation in a single forward pass. Experiments demonstrate competitive editing fidelity and substantially improved cross-view consistency compared to optimization-based methods, while reducing inference time by orders of magnitude.

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