Rethinking Driving World Model as Synthetic Data Generator for
  Perception Tasks
Authors: name: Kai Zeng; name: Zhanqian Wu; name: Kaixin Xiong; name: Xiaobao Wei; name: Xiangyu Guo; name: Zhenxin Zhu; name: Kalok Ho; name: Lijun Zhou; name: Bohan Zeng; name: Ming Lu; name: Haiyang Sun; name: Bing Wang; name: Guang Chen; name: Hangjun Ye; name: Wentao Zhang
Published: 2025-10-22 03:02:38

Abstract: 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
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). When we double the epochs in the baseline, the
benefit of synthetic data becomes negligible. To thoroughly demonstrate the
benefit of synthetic data, we introduce Dream4Drive, a novel synthetic data
generation framework designed for enhancing the downstream perception tasks.
Dream4Drive first decomposes the input video into several 3D-aware guidance
maps and subsequently renders the 3D assets onto these guidance maps. Finally,
the driving world model is fine-tuned to produce the edited, multi-view
photorealistic videos, which can be used to train the downstream perception
models. Dream4Drive enables unprecedented flexibility in generating multi-view
corner cases at scale, significantly boosting corner case perception in
autonomous driving. To facilitate future research, we also contribute a
large-scale 3D asset dataset named DriveObj3D, covering the typical categories
in driving scenarios and enabling diverse 3D-aware video editing. We conduct
comprehensive experiments to show that Dream4Drive can effectively boost the
performance of downstream perception models under various training epochs.
Page: https://wm-research.github.io/Dream4Drive/ GitHub Link:
https://github.com/wm-research/Dream4Drive