Implicit surface representations are valued for their compactness and continuity but pose significant challenges for editing. To address this gap, we introduce NeuSEditor, a novel method for text-guided editing of neural implicit surfaces derived from multi-view images. NeuSEditor employs an identity-preserving architecture that efficiently separates scenes into foreground and background, enabling precise modifications without altering the scene's inherent properties. Our geometry-aware distillation loss significantly enhances rendering and geometric quality. Our method simplifies the editing workflow by eliminating the need for continuous dataset updates and source prompting. NeuSEditor outperforms recent state-of-the-art methods like PDS and InstructNeRF2NeRF, delivering superior rendering and geometric quality.
@InProceedings{...,
author = {Author#1, Author#2, Author#3},
title = {NeuSEditor: From Multi-View Images to Text-Guided Neural Surface Edits},
booktitle = {},
year = {2025},
}