DriveGen3D: Boosting Feed-Forward Driving Scene Generation with
  Efficient Video Diffusion
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.
68f625758589920bf4d322d8
Weijie Wang
claimed_verified
2025-10-20T12:09:14.843Z
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Weijie Wang
user
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Jiagang Zhu
claimed_verified
2025-10-24T16:14:01.264Z
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jiagangzhu
user
hugjiagangzhu
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Zeyu Zhang
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Xiaofeng Wang
claimed_verified
2025-10-23T15:01:02.546Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/6426616ea5ec4a5cbc535634/hH6JsxnXeakH3mBTeNNmO.jpeg
JeffWang
user
Jeff-Wang
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Zheng Zhu
68f625758589920bf4d322dd
Guosheng Zhao
68f625758589920bf4d322de
Chaojun Ni
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Haoxiao Wang
68f625758589920bf4d322e0
Guan Huang
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Xinze Chen
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Yukun Zhou
68f625758589920bf4d322e3
Wenkang Qin
68f625758589920bf4d322e4
Duochao Shi
68f625758589920bf4d322e5
Haoyun Li
68f625758589920bf4d322e6
Guanghong Jia
68f625758589920bf4d322e7
Jiwen Lu
DriveGen3D: Boosting Feed-Forward Driving Scene Generation with
  Efficient Video Diffusion
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.
DriveGen3D generates high-quality, controllable dynamic 3D driving scenes using a unified pipeline with efficient video diffusion and 3D reconstruction.
video diffusion transformer FastDrive-DiT FastRecon3D 3D Gaussian representations SSIM PSNR parameter efficiency