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.
我们提出了一种用于3D场景编辑的新方法，基于扩散模型，旨在确保视角一致性和真实感。该方法利用从单个参考图像中提取的注意力特征来定义目标编辑，并通过与由高斯投影深度估计得出的场景几何对齐，将这些特征在多个视角中进行变换。将变换后的特征注入其他视角，使得编辑能够在3D空间中以高保真和空间对齐的方式传播。 广泛的评估表明，我们的方法在生成多样化的3D场景编辑方面表现出色，与现有方法相比显著提升了场景操作的能力，为3D场景编辑技术带来了新的突破。