ShareVerse: Multi-Agent Consistent Video Generation for Shared World Modeling
This work addresses the gap in existing video generation frameworks for multi-agent interaction, providing a solution for shared world modeling in applications such as autonomous driving or robotics.
ShareVerse tackles the problem of multi-agent shared world modeling in video generation, achieving consistent shared world modeling and accurately perceiving the position of dynamic agents. The framework supports 49-frame large-scale video generation.
This paper presents ShareVerse, a video generation framework enabling multi-agent shared world modeling, addressing the gap in existing works that lack support for unified shared world construction with multi-agent interaction. ShareVerse leverages the generation capability of large video models and integrates three key innovations: 1) A dataset for large-scale multi-agent interactive world modeling is built on the CARLA simulation platform, featuring diverse scenes, weather conditions, and interactive trajectories with paired multi-view videos (front/ rear/ left/ right views per agent) and camera data. 2) We propose a spatial concatenation strategy for four-view videos of independent agents to model a broader environment and to ensure internal multi-view geometric consistency. 3) We integrate cross-agent attention blocks into the pretrained video model, which enable interactive transmission of spatial-temporal information across agents, guaranteeing shared world consistency in overlapping regions and reasonable generation in non-overlapping regions. ShareVerse, which supports 49-frame large-scale video generation, accurately perceives the position of dynamic agents and achieves consistent shared world modeling.