  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.
