Rethinking Driving World Model as Synthetic Data Generator for Perception Tasks

Understand

Recent advancements in driving world models enable controllable generation of high-quality RGB videos or multimodal videos.

• Existing methods primarily focus on metrics related to generation quality and controllability.
• However, they often overlook the evaluation of downstream perception tasks, which are $\mathbf{really\ crucial}$ for the performance of autonomous driving.
• Existing methods usually leverage a training strategy that first pretrains on synthetic data and finetunes on real data, resulting in twice the epochs compared to the baseline (real data only).