LangEditor: Natural Language-Driven 4D Editing for Improved Controllability of Dynamic Driving Scenes

Abstract

Diverse and realistic data are essential for developing reliable autonomous driving (AD) systems, yet collecting and annotating large-scale real-world driving datasets is costly and time-consuming. Recent advances in synthetic scene generation and editing have enabled the creation of diverse driving scenarios. However, fully synthetic scenes often lack real-world grounding, while existing editing approaches are either limited to pure video manipulation or involve cumbersome manual operations. To solve this, we present LangEditor, the natural language-driven 4D editing framework for dynamic driving scenes. LangEditor automatically grounds free-form language instructions to target vehicles and their editable attributes, generating physically plausible trajectories consistent with scene semantics. To ensure spatiotemporal coherence and visual fidelity, we propose a joint refinement strategy that integrates a Dynamic Illumination-Aware Shadow Module for lighting consistency across space-time, and an Appearance Refinement module for synthesizing high-quality textures and material properties. Extensive experiments on realistic driving datasets demonstrate that LangEditor enables intuitive, fine-grained, and photorealistic 4D scene manipulation, outperforming existing baselines in both editing quality and controllability. Our approach bridges the gap between realistic scene editing and user-friendly controllability, offering a powerful tool for data augmentation and simulation in AD research.