仅用少量样本即可灵活编辑3D人脸，支持自定义布局控制。
FFaceNeRF: Few-shot Face Editing in Neural Radiance Fields
- 通过特征注入的几何适配器实现对人脸结构的精准调控。
- 结合潜在空间混合技术，仅需少量样本即可完成模型训练。
- 适合个性化医疗影像与创意人脸编辑等需要高自由度的应用。
基于神经辐射场（NeRF）的近期3D人脸编辑方法虽能生成高质量图像，但受限于预训练分割掩码的固定布局，用户控制能力较弱。为解决这一问题，本文提出FFaceNeRF，一种基于NeRF的人脸编辑技术，可实现对任意期望布局掩码的快速适应。该方法引入几何适配器与特征注入机制，有效操控人脸几何属性；同时采用潜在空间混合进行三平面增强，显著降低对训练数据量的需求。实验表明，该方法在灵活性、控制精度与图像质量方面均优于现有基于掩码的编辑方法，适用于个性化医疗成像与创意人脸编辑等场景。代码已公开于项目主页。
原文摘要 · Abstract (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 {\href{https://kwanyun.github.io/FFaceNeRF_page/}{project-page}}.
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