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arXiv · 2604.20038

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

Haitao Huang·Shin-Fang Chng·Huangying Zhan·Qingan Yan·Yi Xu
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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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Haitao Huang, Shin-Fang Chng, Huangying Zhan, Qingan Yan, Yi Xu. 2026-04-21. FluSplat: Sparse-View 3D Editing without Test-Time Optimization. https://arxiv.org/abs/2604.20038

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