Abstract
We present GenN2N, a unified NeRF-to-NeRF translation framework for various NeRF translation tasks such as text-driven NeRF editing, colorization, super-resolution, inpainting, etc. Unlike previous methods designed for individual translation tasks with task-specific schemes, GenN2N achieves all these NeRF editing tasks by employing a plug-and-play image-to-image translator to perform editing in the 2D domain and lifting 2D edits into the 3D NeRF space.
Since the 3D consistency of 2D edits may not be assured, we propose to model the distribution of the underlying 3D edits through a generative model that can cover all possible edited NeRFs. To model the distribution of 3D edited NeRFs from 2D edited images, we carefully design a VAE-GAN that encodes images while decoding NeRFs. The latent space is trained to align with a Gaussian distribution and the NeRFs are supervised through an adversarial loss on its renderings. To ensure the latent code does not depend on 2D viewpoints but truly reflects the 3D edits, we also regularize the latent code through a contrastive learning scheme.
Extensive experiments on various editing tasks show GenN2N, as a universal framework, performs as well or better than task-specific specialists while possessing flexible generative power.
Overview
Our Results
Text-driven Editing
Colorization
Super-resolution
Inpainting
Zoom Out
Comparsions
Text-driven Editing
Our Interpolation
BibTeX
@article{liu2024genn2n,
title={GenN2N: Generative NeRF2NeRF Translation},
author={Liu, Xiangyue and Xue, Han and Luo, Kunming and Tan, Ping and Yi, Li},
journal={arXiv preprint arXiv:2404.02788},
year={2024}
}