ViCA-NeRF: View-Consistency-Aware 3D Editing of Neural Radiance Fields

We introduce ViCA-NeRF, the first view-consistency-aware method for 3D editing with text instructions. In addition to the implicit neural radiance field (NeRF) modeling, our key insight is to exploit two sources of regularization that explicitly propagate the editing information across different views, thus ensuring multi-view consistency. For geometric regularization, we leverage the depth information derived from NeRF to establish image correspondences between different views. For learned regularization, we align the latent codes in the 2D diffusion model between edited and unedited images, enabling us to edit key views and propagate the update throughout the entire scene. Incorporating these two strategies, our ViCA-NeRF operates in two stages.