Recent progress in driving video generation has shown significant potential for enhancing self-driving systems by providing scalable and controllable training data. Although pretrained state-of-the-art generation models, guided by 2D layout conditions (e.g., HD maps and bounding boxes), can produce photorealistic driving videos, achieving controllable multi-view videos with high 3D consistency remains a major challenge. To tackle this, we introduce a novel spatial adaptive generation framework, CoGen, which leverages advances in 3D generation to improve performance in two key aspects: (i) To ensure 3D consistency, we first generate high-quality, controllable 3D conditions that capture the geometry of driving scenes. By replacing coarse 2D conditions with these fine-grained 3D representations, our approach significantly enhances the spatial consistency of the generated videos. (ii) Additionally, we introduce a consistency adapter module to strengthen the robustness of the model to multi-condition control. The results demonstrate that this method excels in preserving geometric fidelity and visual realism, offering a reliable video generation solution for autonomous driving.
View on arXiv@article{ji2025_2503.22231,
title={ CoGen: 3D Consistent Video Generation via Adaptive Conditioning for Autonomous Driving },
author={ Yishen Ji and Ziyue Zhu and Zhenxin Zhu and Kaixin Xiong and Ming Lu and Zhiqi Li and Lijun Zhou and Haiyang Sun and Bing Wang and Tong Lu },
journal={arXiv preprint arXiv:2503.22231},
year={ 2025 }
}