FaceDNeRF：基于扩散模型的语义驱动人脸重建、提示编辑与重光照
FaceDNeRF: Semantics-Driven Face Reconstruction, Prompt Editing and Relighting with Diffusion Models
AI总结：
FaceDNeRF提出一种结合3D GAN反演与潜在扩散模型的单图像人脸NeRF重建方法，支持零样本文本提示编辑和重光照，在真实感和编辑灵活性上优于现有方法。
摘要：
从单张图像创建高质量三维人脸的能力变得越来越重要，在视频会议、AR/VR以及电影行业的高级视频编辑中具有广泛应用。本文提出Face Diffusion NeRF（FaceDNeRF），一种新的生成方法，用于从单张图像重建高质量Face NeRF，并具备语义编辑和重光照能力。FaceDNeRF利用高分辨率3D GAN反演和经过专业训练的2D潜在扩散模型，使用户能够在零样本学习下操作和构建Face NeRF，而无需显式3D数据。通过精心设计的照明和身份保持损失以及多模态预训练，FaceDNeRF为用户提供了对编辑过程前所未有的控制，使他们能够仅使用单视图图像、文本提示和明确的目标光照来创建和编辑人脸NeRF。FaceDNeRF的高级特性旨在产生比依赖2D分割图进行可编辑属性的现有2D编辑方法更令人印象深刻的结果。实验表明，与最先进的3D人脸重建和编辑方法相比，我们的FaceDNeRF实现了异常逼真的结果和前所未有的编辑灵活性。我们的代码将在https://github.com/BillyXYB/FaceDNeRF提供。
英文摘要：
The ability to create high-quality 3D faces from a single image has become increasingly important with wide applications in video conferencing, AR/VR, and advanced video editing in movie industries. In this paper, we propose Face Diffusion NeRF (FaceDNeRF), a new generative method to reconstruct high-quality Face NeRFs from single images, complete with semantic editing and relighting capabilities. FaceDNeRF utilizes high-resolution 3D GAN inversion and expertly trained 2D latent-diffusion model, allowing users to manipulate and construct Face NeRFs in zero-shot learning without the need for explicit 3D data. With carefully designed illumination and identity preserving loss, as well as multi-modal pre-training, FaceDNeRF offers users unparalleled control over the editing process enabling them to create and edit face NeRFs using just single-view images, text prompts, and explicit target lighting. The advanced features of FaceDNeRF have been designed to produce more impressive results than existing 2D editing approaches that rely on 2D segmentation maps for editable attributes. Experiments show that our FaceDNeRF achieves exceptionally realistic results and unprecedented flexibility in editing compared with state-of-the-art 3D face reconstruction and editing methods. Our code will be available at https://github.com/BillyXYB/FaceDNeRF.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
