DrivingDiffusion: Layout-Guided multi-view driving scene video
  generation with latent diffusion model
Xiaofan Li Yifu Zhang Xiaoqing Ye
With the increasing popularity of autonomous driving based on the powerful
and unified bird's-eye-view (BEV) representation, a demand for high-quality and
large-scale multi-view video data with accurate annotation is urgently
required. However, such large-scale multi-view data is hard to obtain due to
expensive collection and annotation costs. To alleviate the problem, we propose
a spatial-temporal consistent diffusion framework DrivingDiffusion, to generate
realistic multi-view videos controlled by 3D layout. There are three challenges
when synthesizing multi-view videos given a 3D layout: How to keep 1)
cross-view consistency and 2) cross-frame consistency? 3) How to guarantee the
quality of the generated instances? Our DrivingDiffusion solves the problem by
cascading the multi-view single-frame image generation step, the single-view
video generation step shared by multiple cameras, and post-processing that can
handle long video generation. In the multi-view model, the consistency of
multi-view images is ensured by information exchange between adjacent cameras.
In the temporal model, we mainly query the information that needs attention in
subsequent frame generation from the multi-view images of the first frame. We
also introduce the local prompt to effectively improve the quality of generated
instances. In post-processing, we further enhance the cross-view consistency of
subsequent frames and extend the video length by employing temporal sliding
window algorithm. Without any extra cost, our model can generate large-scale
realistic multi-camera driving videos in complex urban scenes, fueling the
downstream driving tasks. The code will be made publicly available.