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

SII-Kang He, Wenshuo Peng, Chuanhao Li, Zihui Gao, Xiaojie Xu, Zhengyuan Lin, Yuzhe Ding, Donghong Ji, Kaipeng Zhang, Yongtao Ge

GameVerse: A Minute-Scale Gameplay Dataset for Long-Horizon Interactive World Modeling: 37 upvotes on Hugging Face Daily Papers, #5 of 38 papers on 2026-11-05. Day-by-day upvote history.

Upvotes over time

It had 34 upvotes by its first day, 38 upvotes by the next day, 37 upvotes by day 3, 37 upvotes by day 7, and has 37 now. It ranked #33 of 207 papers in its week and #49 of 340 in its month. Its code repository has 15 GitHub stars.

Recent advances in interactive world models have made it possible to generate controllable, dynamic environments from action inputs. Yet current models struggle to maintain coherence beyond roughly one minute of rollout, a limitation rooted not in model capacity but in the scarcity of long-horizon training data. Existing gameplay video datasets typically consist of clips under ten seconds, with captions that describe whole clips in coarse and undifferentiated text, which is insufficient for controllable long-horizon generation. We address this with GameVerse, a minute-scale gameplay video dataset purpose-built for long-horizon interactive world modeling. GameVerse contains 69.7K clips spanning 1,655 hours and 349.6 million frames across diverse first- and third-person gameplay, including 1,358 hours of continuous two-minute sequences, an order of magnitude longer than prior datasets. Clips pass through a unified pipeline of scene-consistent segmentation, quality filtering, camera pose estimation, and event-based caption annotation. Each clip is paired with a temporally stable camera trajectory that remains coherent across minutes, together with captions that separate static scene context from dynamic foreground events and align to event boundaries rather than fixed time windows. Comprehensive analysis shows that GameVerse surpasses existing public datasets in scale, diversity, and annotation quality. Experiments further demonstrate that training on GameVerse substantially extends the temporal horizon of interactive world models.

Paper page on Hugging Face · arXiv

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