用多视角图像实现文本引导的神经表面编辑，保留身份且几何更准确
NeuSEditor: From Multi-View Images to Text-Guided Neural Surface Edits
- 分离前景背景，保持场景元素不变
- 几何感知蒸馏损失提升渲染与几何质量
- 无需持续更新数据集，适合快速编辑
隐式表面表示因其紧凑性和连续性受到重视，但编辑难度大。现有方法常难以保持身份一致性和几何一致性。为此，我们提出NeuSEditor，一种基于多视角图像的文本引导神经隐式表面编辑方法。该方法采用保持身份的架构，高效分离前景与背景，实现精准修改而不改变场景特有元素。引入几何感知蒸馏损失，显著提升渲染和几何质量。简化编辑流程，无需持续更新数据集或源提示。NeuSEditor在定量和定性上均优于PDS和InstructNeRF2NeRF等先进方法。
原文摘要 · Abstract (English)
Implicit surface representations are valued for their compactness and continuity, but they pose significant challenges for editing. Despite recent advancements, existing methods often fail to preserve identity and maintain geometric consistency during editing. To address these challenges, we present NeuSEditor, a novel method for text-guided editing of neural implicit surfaces derived from multi-view images. NeuSEditor introduces an identity-preserving architecture that efficiently separates scenes into foreground and background, enabling precise modifications without altering the scene-specific elements. 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 quantitative and qualitative results. For more visual results, visit: neuseditor.github.io.
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