InstaDrive: Instance-Aware Driving World Models for Realistic and Consistent Video Generation

Zhuoran Yang, Xi Guo, Chenjing Ding, Chiyu Wang, Wei Wu, Yanyong Zhang; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 2025, pp. 25410-25420

Autonomous driving relies on robust models trained on high-quality, large-scale multi-view driving videos for tasks like perception and planning. While world models offer a cost-effective solution for generating realistic driving videos, they struggle to maintain instance-level temporal consistency and spatial geometric fidelity. To address these challenges, we propose **InstaDrive**, a novel framework that enhances driving video realism through two key advancements: (1) Instance Flow Guider module, which extracts and propagates instance features across frames to enforce temporal consistency, preserving instance identity over time. (2) Spatial Geometric Aligner module, which improves spatial reasoning, ensures precise instance positioning, and explicitly models occlusion hierarchies. By incorporating these instance-aware mechanisms, InstaDrive achieves state-of-the-art video generation quality and enhances downstream autonomous driving tasks on the nuScenes dataset. Additionally, we utilize CARLA's autopilot to procedurally and stochastically simulate rare yet safety-critical driving scenarios across diverse maps and regions, enabling rigorous safety evaluation for autonomous systems.