FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
This work addresses the challenge of personalized and high-fidelity 3D face editing for applications like medical imaging or creative design, though it appears incremental as it builds on existing NeRF-based methods.
The paper tackles the problem of limited user control in 3D face editing with Neural Radiance Fields by introducing FFaceNeRF, which uses a geometry adapter and latent mixing to enable training with few samples and surpasses existing methods in flexibility, control, and image quality.
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 {\href{https://kwanyun.github.io/FFaceNeRF_page/}{project-page}}.