GameVerse: A Minute-Scale Gameplay Dataset for Long-Horizon Interactive World Modeling

Recent advances in world models have emphasized the need for learning temporally consistent and interactive representations of dynamic environments. However, existing video datasets fail to simultaneously support long-horizon dynamics, precise camera control, and structured semantic representations. To address this, we introduce GameVerse, a large-scale game video dataset for interactive world modeling. GameVerse provides minute-level continuous video sequences, covering diverse interaction behaviors, across both first- and third-person perspectives. The dataset is constructed through a unified data processing pipeline, including scene-consistent segmentation, quality filtering, camera pose estimation, and multi-granular semantic annotation. GameVerse consists of 11,092 videos, with a total duration of 4,692 hours (195.5 days) and approximately 986.6 million frames. Each video segment is augmented with temporally stable camera trajectories and structured semantic annotations. Comprehensive data analysis and experiments demonstrate that GameVerse exhibits strong advantages in terms of scale, diversity, and annotation quality, and shows promising performance for long-horizon modeling and interactive world understanding. We expect GameVerse to provide a unified data foundation for long-horizon modeling and interactive world modeling, and to facilitate future research in this area.