NeuSEditor: From Multi-View Images to Text-Guided Neural Surface Edits

Nail Ibrahimli, Julian Kooij, Liangliang Nan

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. Project page: https://neuseditor.github.io/