Generating multi-camera street-view videos is critical for augmenting autonomous driving datasets, addressing the urgent demand for extensive and varied data. Due to the limitations in diversity and challenges in handling lighting conditions, traditional rendering-based methods are increasingly being supplanted by diffusion-based methods. However, a significant challenge in diffusion-based methods is ensuring that the generated sensor data preserve both intra-world consistency and inter-sensor coherence. To address these challenges, we combine an additional explicit world volume and propose the World Volume-aware Multi-camera Driving Scene Generator (WoVoGen). This system is specifically designed to leverage 4D world volume as a foundational element for video generation. Our model operates in two distinct phases: (i) envisioning the future 4D temporal world volume based on vehicle control sequences, and (ii) generating multi-camera videos, informed by this envisioned 4D temporal world volume and sensor interconnectivity. The incorporation of the 4D world volume empowers WoVoGen not only to generate high-quality street-view videos in response to vehicle control inputs but also to facilitate scene editing tasks.

翻译：生成多相机街景视频对于扩充自动驾驶数据集至关重要，可满足对海量多样化数据的迫切需求。由于多样性受限且难以处理光照条件，传统基于渲染的方法正逐渐被基于扩散的方法所取代。然而，基于扩散的方法面临一项重大挑战：如何确保生成的传感器数据在保持世界内部一致性的同时，又能维持传感器间的连贯性。为应对这些挑战，我们引入显式世界体作为补充，并提出世界体感知多相机驾驶场景生成器（WoVoGen）。该系统专为利用4D世界体作为视频生成的基础要素而设计。模型分两个阶段运行：（i）基于车辆控制序列预想未来4D时序世界体，（ii）根据预想的4D时序世界体与传感器互联性生成多相机视频。4D世界体的集成使WoVoGen不仅能根据车辆控制输入生成高质量街景视频，还能支持场景编辑任务。