FFaceNeRF：神經輻射場中的少樣本人臉編輯

FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields

March 21, 2025

作者: Kwan Yun, Chaelin Kim, Hangyeul Shin, Junyong Noh

cs.AI

摘要

近期利用遮罩進行3D臉部編輯的方法，通過運用神經輻射場（NeRF）技術，已能生成高品質的編輯圖像。儘管這些方法表現出色，但由於使用預訓練的分割遮罩，現有方法往往提供有限的用戶控制。要利用具有理想佈局的遮罩，需要大量的訓練數據集，而這在收集上具有挑戰性。我們提出了FFaceNeRF，這是一種基於NeRF的臉部編輯技術，能夠克服因使用固定遮罩佈局而導致的用戶控制受限問題。我們的方法採用了帶有特徵注入的幾何適配器，有效操控幾何屬性。此外，我們採用潛在混合進行三平面增強，使得僅需少量樣本即可完成訓練。這促進了模型快速適應所需的遮罩佈局，對於個性化醫療影像或創意臉部編輯等領域的應用至關重要。我們的比較評估顯示，FFaceNeRF在靈活性、控制力及生成圖像質量方面均超越了現有的基於遮罩的臉部編輯方法，為未來定制化及高保真3D臉部編輯的進步鋪平了道路。代碼可在{https://kwanyun.github.io/FFaceNeRF_page/{項目頁面}}獲取。

English

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}}.