用扩散模型实现3D场景编辑，保持多视角一致性。
Diffusion-Based Attention Warping for Consistent 3D Scene Editing
- 通过单图注意力特征，跨视角传播编辑内容。
- 利用高斯点云深度对齐几何，实现特征精准映射。
- 适合需要高质量3D编辑的视觉生成研究者。
我们提出一种基于扩散模型的3D场景编辑方法，旨在确保不同视角间的一致性与真实感。该方法从单张参考图像提取注意力特征，并通过与高斯点云估计的场景深度对齐，将这些特征在多视角间进行几何引导的扭曲。将扭曲后的特征注入其他视图，实现了3D空间中编辑内容的高保真与空间对齐。大量实验表明，该方法在生成多样化3D场景编辑结果方面表现优异，显著优于现有方法。项目页面：https://attention-warp.github.io
原文摘要 · Abstract (English)
We present a novel method for 3D scene editing using diffusion models, designed to ensure view consistency and realism across perspectives. Our approach leverages attention features extracted from a single reference image to define the intended edits. These features are warped across multiple views by aligning them with scene geometry derived from Gaussian splatting depth estimates. Injecting these warped features into other viewpoints enables coherent propagation of edits, achieving high fidelity and spatial alignment in 3D space. Extensive evaluations demonstrate the effectiveness of our method in generating versatile edits of 3D scenes, significantly advancing the capabilities of scene manipulation compared to the existing methods. Project page: \url{https://attention-warp.github.io}
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