NeRFEditor: Differentiable Style Decomposition for Full 3D Scene Editing

Understand

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.