FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
Authors: name: Kwan Yun, name: Chaelin Kim, name: Hangyeul Shin, name: Junyong Noh

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}}.