Editing 4D scenes reconstructed from monocular videos based on text prompts is a valuable yet challenging task with broad applications in content creation and virtual environments. The key difficulty lies in achieving semantically precise edits in localized regions of complex, dynamic scenes, while preserving the integrity of unedited content. To address this, we introduce Mono4DEditor, a novel framework for flexible and accurate text-driven 4D scene editing. Our method augments 3D Gaussians with quantized CLIP features to form a language-embedded dynamic representation, enabling efficient semantic querying of arbitrary spatial regions. We further propose a two-stage point-level localization strategy that first selects candidate Gaussians via CLIP similarity and then refines their spatial extent to improve accuracy. Finally, targeted edits are performed on localized regions using a diffusion-based video editing model, with flow and scribble guidance ensuring spatial fidelity and temporal coherence. Extensive experiments demonstrate that Mono4DEditor enables high-quality, text-driven edits across diverse scenes and object types, while preserving the appearance and geometry of unedited areas and surpassing prior approaches in both flexibility and visual fidelity.

翻译：基于文本提示编辑从单目视频重建的4D场景是一项具有广泛应用价值但极具挑战性的任务，其应用涵盖内容创作与虚拟环境构建。核心难点在于如何在复杂动态场景的局部区域实现语义精确的编辑，同时保持未编辑内容的完整性。为此，我们提出了Mono4DEditor，一个用于实现灵活、准确的文本驱动4D场景编辑的新型框架。我们的方法通过量化CLIP特征增强3D高斯，构建了一种语言嵌入的动态表示，从而能够对任意空间区域进行高效的语义查询。我们进一步提出了一种两阶段的点级定位策略：首先通过CLIP相似性筛选候选高斯点，随后细化其空间范围以提升定位精度。最后，利用基于扩散的视频编辑模型对定位区域进行针对性编辑，并通过光流与涂鸦引导确保空间保真度与时间连贯性。大量实验表明，Mono4DEditor能够在多样化场景与对象类型上实现高质量的文本驱动编辑，在保持未编辑区域外观与几何结构的同时，在灵活性与视觉保真度上均超越了现有方法。