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Generating multi-camera street-view videos is critical for augmentingautonomous driving datasets, addressing the urgent demand for extensive andvaried data. Due to the limitations in diversity and challenges in handlinglighting conditions, traditional rendering-based methods are increasingly beingsupplanted by diffusion-based methods. However, a significant challenge indiffusion-based methods is ensuring that the generated sensor data preserveboth intra-world consistency and inter-sensor coherence. To address thesechallenges, we combine an additional explicit world volume and propose theWorld Volume-aware Multi-camera Driving Scene Generator (WoVoGen). This systemis specifically designed to leverage 4D world volume as a foundational elementfor video generation. Our model operates in two distinct phases: (i)envisioning the future 4D temporal world volume based on vehicle controlsequences, and (ii) generating multi-camera videos, informed by this envisioned4D temporal world volume and sensor interconnectivity. The incorporation of the4D world volume empowers WoVoGen not only to generate high-quality street-viewvideos in response to vehicle control inputs but also to facilitate sceneediting tasks.

WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene Generation