FluSplat：无需测试时优化的稀疏视角3D编辑
FluSplat: Sparse-View 3D Editing without Test-Time Optimization
AI总结：
本文提出一种无需测试时优化的稀疏视角3D编辑框架，通过图像域中的跨视角正则化方案实现视图一致性，提升编辑质量和效率。
摘要：
近期文本引导图像编辑和3D高斯散射（3DGS）的进步使得高质量3D场景操控成为可能。然而，现有流程依赖于测试时的迭代编辑和拟合优化，交替进行2D扩散编辑和3D重建。此过程计算成本高、场景特定且易产生跨视角不一致。我们提出一种前馈框架，从稀疏视角实现跨视角一致的3D场景编辑。不同于通过迭代3D细化强制一致性，我们引入图像域中的跨视角正则化方案进行训练。通过联合监督多视角编辑与几何对齐约束，模型在推理时无需每场景优化即可产生视图一致的结果。编辑视图随后通过前馈3DGS模型提升至3D，从而在单次正向传递中获得一致的3DGS表示。实验表明，与基于优化的方法相比，该方法在编辑保真度和跨视角一致性方面表现更优，同时将推理时间减少多个数量级。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
