title: FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
summary: Recent 3D face editing methods using masks have produced high-quality edited
  images by leveraging Neural Radiance Fields (NeRF). Despite their impressive
  performance, existing methods often provide limited user control due to the use
  of pre-trained segmentation masks. To utilize masks with a desired layout, an
  extensive training dataset is required, which is challenging to gather. We
  present FFaceNeRF, a NeRF-based face editing technique that can overcome the
  challenge of limited user control due to the use of fixed mask layouts. Our
  method employs a geometry adapter with feature injection, allowing for
  effective manipulation of geometry attributes. Additionally, we adopt latent
  mixing for tri-plane augmentation, which enables training with a few samples.
  This facilitates rapid model adaptation to desired mask layouts, crucial for
  applications in fields like personalized medical imaging or creative face
  editing. Our comparative evaluations demonstrate that FFaceNeRF surpasses
  existing mask based face editing methods in terms of flexibility, control, and
  generated image quality, paving the way for future advancements in customized
  and high-fidelity 3D face editing. The code is available on the
  {https://kwanyun.github.io/FFaceNeRF_page/{project-page}}.
paper_authors._id: 67e147d28e8ab85fb6d72244
paper_authors.name: Kwan Yun
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-03-24T12:57:18.816Z
paper_authors._id: 639d445524af4747d8d2af52
paper_authors.avatarUrl: /avatars/6ca870edc993fd3db6b0db4e4848ef7a.svg
paper_authors.fullname: kwan yun
paper_authors.type: user
paper_authors.user: kwanY
paper_authors._id: 67e147d28e8ab85fb6d72245
paper_authors.name: Chaelin Kim
paper_authors._id: 67e147d28e8ab85fb6d72246
paper_authors.name: Hangyeul Shin
paper_authors._id: 67e147d28e8ab85fb6d72247
paper_authors.name: Junyong Noh
paper_title: FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
paper_summary: Recent 3D face editing methods using masks have produced high-quality edited
  images by leveraging Neural Radiance Fields (NeRF). Despite their impressive
  performance, existing methods often provide limited user control due to the use
  of pre-trained segmentation masks. To utilize masks with a desired layout, an
  extensive training dataset is required, which is challenging to gather. We
  present FFaceNeRF, a NeRF-based face editing technique that can overcome the
  challenge of limited user control due to the use of fixed mask layouts. Our
  method employs a geometry adapter with feature injection, allowing for
  effective manipulation of geometry attributes. Additionally, we adopt latent
  mixing for tri-plane augmentation, which enables training with a few samples.
  This facilitates rapid model adaptation to desired mask layouts, crucial for
  applications in fields like personalized medical imaging or creative face
  editing. Our comparative evaluations demonstrate that FFaceNeRF surpasses
  existing mask based face editing methods in terms of flexibility, control, and
  generated image quality, paving the way for future advancements in customized
  and high-fidelity 3D face editing. The code is available on the
  {https://kwanyun.github.io/FFaceNeRF_page/{project-page}}.
paper_ai_summary: FFaceNeRF enhances 3D face editing with geometry adaptation and tri-plane augmentation, improving flexibility, control, and image quality compared to existing mask-based methods.
paper_ai_keywords: Neural Radiance Fields NeRF geometry adapter feature injection latent mixing tri-plane augmentation