This paper enables high-fidelity, transferable NeRF editing by frequency decomposition. Recent NeRF editing pipelines lift 2D stylization results to 3D scenes while suffering from blurry results, and fail to capture detailed structures caused by the inconsistency between 2D editings. Our critical insight is that low-frequency components of images are more multiview-consistent after editing compared with their high-frequency parts. Moreover, the appearance style is mainly exhibited on the low-frequency components, and the content details especially reside in high-frequency parts. This motivates us to perform editing on low-frequency components, which results in high-fidelity edited scenes. In addition, the editing is performed in the low-frequency feature space, enabling stable intensity control and novel scene transfer. Comprehensive experiments conducted on photorealistic datasets demonstrate the superior performance of high-fidelity and transferable NeRF editing. The project page is at \url{https://aigc3d.github.io/freditor}.

翻译：本文通过频率分解实现了高保真且可迁移的NeRF编辑。现有NeRF编辑流程将2D风格化结果提升至3D场景，但常因2D编辑间的不一致性而导致输出模糊，难以捕捉精细结构。我们的关键洞见在于：编辑后图像的低频成分相比高频部分具有更强的多视角一致性。此外，视觉风格主要表现于低频成分，而内容细节则集中在高频部分。这一发现促使我们在低频成分上进行编辑，从而获得高保真度的编辑场景。同时，在低频特征空间中执行的编辑操作支持稳定的强度控制与新颖场景迁移。在逼真数据集上的综合实验表明，本方法在高保真与可迁移NeRF编辑方面具有优越性能。项目页面访问：\url{https://aigc3d.github.io/freditor}。