  We present NeRFEditor, an efficient learning framework for 3D scene editing,
which takes a video captured over 360{\deg} as input and outputs a
high-quality, identity-preserving stylized 3D scene. Our method supports
diverse types of editing such as guided by reference images, text prompts, and
user interactions. We achieve this by encouraging a pre-trained StyleGAN model
and a NeRF model to learn from each other mutually. Specifically, we use a NeRF
model to generate numerous image-angle pairs to train an adjustor, which can
adjust the StyleGAN latent code to generate high-fidelity stylized images for
any given angle. To extrapolate editing to GAN out-of-domain views, we devise
another module that is trained in a self-supervised learning manner. This
module maps novel-view images to the hidden space of StyleGAN that allows
StyleGAN to generate stylized images on novel views. These two modules together
produce guided images in 360{\deg}views to finetune a NeRF to make stylization
effects, where a stable fine-tuning strategy is proposed to achieve this.
Experiments show that NeRFEditor outperforms prior work on benchmark and
real-world scenes with better editability, fidelity, and identity preservation.
