Freditor: High-Fidelity and Transferable NeRF Editing by Frequency
  Decomposition
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 https://aigc3d.github.io/freditor.
660e44a31a20b667fcb380d9
Yisheng He
660e44a31a20b667fcb380da
Weihao Yuan
660e44a31a20b667fcb380db
Siyu Zhu
660e44a31a20b667fcb380dc
Zilong Dong
660e44a31a20b667fcb380dd
Liefeng Bo
660e44a31a20b667fcb380de
Qixing Huang
claimed_verified
2025-04-08T07:01:52.354Z
67f18d051358145ee5bb3a11
/avatars/e5a8c12295d7609625c2b891d47c3112.svg
qixing huang
user
qixinghuang1
Freditor: High-Fidelity and Transferable NeRF Editing by Frequency
  Decomposition
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 https://aigc3d.github.io/freditor.
NeRF editing achieves high-fidelity results by decomposing images into low-frequency and high-frequency components and editing in the low-frequency space to maintain consistency and detail.
NeRF frequency decomposition multiview-consistent low-frequency components high-frequency components scene transfer feature space intensity control