FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
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}}.
67e147d28e8ab85fb6d72244
Kwan Yun
admin_assigned
2025-03-24T12:57:18.816Z
639d445524af4747d8d2af52
/avatars/6ca870edc993fd3db6b0db4e4848ef7a.svg
kwan yun
user
kwanY
67e147d28e8ab85fb6d72245
Chaelin Kim
67e147d28e8ab85fb6d72246
Hangyeul Shin
67e147d28e8ab85fb6d72247
Junyong Noh
FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
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}}.
FFaceNeRF enhances 3D face editing with geometry adaptation and tri-plane augmentation, improving flexibility, control, and image quality compared to existing mask-based methods.
Neural Radiance Fields NeRF geometry adapter feature injection latent mixing tri-plane augmentation