DriveGen3D: Boosting Feed-Forward Driving Scene Generation with
  Efficient Video Diffusion
Authors: name: Weijie Wang, name: Jiagang Zhu, name: Zeyu Zhang, name: Xiaofeng Wang, name: Zheng Zhu, name: Guosheng Zhao, name: Chaojun Ni, name: Haoxiao Wang, name: Guan Huang, name: Xinze Chen, name: Yukun Zhou, name: Wenkang Qin, name: Duochao Shi, name: Haoyun Li, name: Guanghong Jia, name: Jiwen Lu
Organization: GigaAI-Research

We present DriveGen3D, a novel framework for generating high-quality and
highly controllable dynamic 3D driving scenes that addresses critical
limitations in existing methodologies. Current approaches to driving scene
synthesis either suffer from prohibitive computational demands for extended
temporal generation, focus exclusively on prolonged video synthesis without 3D
representation, or restrict themselves to static single-scene reconstruction.
Our work bridges this methodological gap by integrating accelerated long-term
video generation with large-scale dynamic scene reconstruction through
multimodal conditional control. DriveGen3D introduces a unified pipeline
consisting of two specialized components: FastDrive-DiT, an efficient video
diffusion transformer for high-resolution, temporally coherent video synthesis
under text and Bird's-Eye-View (BEV) layout guidance; and FastRecon3D, a
feed-forward reconstruction module that rapidly builds 3D Gaussian
representations across time, ensuring spatial-temporal consistency. Together,
these components enable real-time generation of extended driving videos (up to
424times800 at 12 FPS) and corresponding dynamic 3D scenes, achieving SSIM
of 0.811 and PSNR of 22.84 on novel view synthesis, all while maintaining
parameter efficiency.