title: Freditor: High-Fidelity and Transferable NeRF Editing by Frequency
    Decomposition
summary: 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.
paper_authors._id: 660e44a31a20b667fcb380d9
paper_authors.name: Yisheng He
paper_authors._id: 660e44a31a20b667fcb380da
paper_authors.name: Weihao Yuan
paper_authors._id: 660e44a31a20b667fcb380db
paper_authors.name: Siyu Zhu
paper_authors._id: 660e44a31a20b667fcb380dc
paper_authors.name: Zilong Dong
paper_authors._id: 660e44a31a20b667fcb380dd
paper_authors.name: Liefeng Bo
paper_authors._id: 660e44a31a20b667fcb380de
paper_authors.name: Qixing Huang
paper_title: Freditor: High-Fidelity and Transferable NeRF Editing by Frequency
    Decomposition
paper_summary: 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.
paper_ai_keywords: NeRF editing frequency decomposition multiview-consistent 2D stylization 3D scenes high-fidelity low-frequency components high-frequency parts intensity control scene transfer