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- W4310995127 abstract "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." @default.
- W4310995127 created "2022-12-22" @default.
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- W4310995127 date "2022-12-07" @default.
- W4310995127 modified "2023-10-17" @default.
- W4310995127 title "NeRFEditor: Differentiable Style Decomposition for Full 3D Scene Editing" @default.
- W4310995127 doi "https://doi.org/10.48550/arxiv.2212.03848" @default.
- W4310995127 hasPublicationYear "2022" @default.
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