WildWorld: A Large-Scale Dataset for Dynamic World Modeling with Actions and Explicit State toward Generative ARPG
Paper • 2603.23497 • Published • 92
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WildWorld: A Large-Scale Dataset for Dynamic World Modeling
with Actions and Explicit State toward Generative ARPG
This repo contains the dataset proposed in
Zhen Li, Zian Meng, Shuwei Shi, Wenshuo Peng, Yuwei Wu, Bo Zheng, Chuanhao Li, Kaipeng Zhang
Alaya Studio, Shanda AI Research Tokyo; Beijing Institute of Technology; Shanghai Innovation Institute
🧠Introduction
TL;DR We present WildWorld, a large-scale action-conditioned world modeling dataset with explicit state annotations, automatically collected from a photorealistic AAA action role-playing game. It features:
- 🎬 108M+ frames with per-frame annotations: character skeletons, actions & states (HP, animation, etc.), camera poses, and depth maps
- ⚔️ 450+ semantically meaningful actions including movement, attacks, and skill casting
- 🐉 Diverse content: 29 monster species, 4 player characters, 4 weapon types, 5 distinct stages
- 🕒 Long-horizon sequences: clips spanning up to 30+ minutes of continuous gameplay
- 📝 Hierarchical captions: both action-level and sample-level natural language descriptions
🚀 Quick Start
Please refer to our Github Repo.
📖Citation
If you find this project helpful, please consider citing:
@article{li2026wildworld,
title={Wildworld: A large-scale dataset for dynamic world modeling with actions and explicit state toward generative arpg},
author={Li, Zhen and Meng, Zian and Shi, Shuwei and Peng, Wenshuo and Wu, Yuwei and Zheng, Bo and Li, Chuanhao and Zhang, Kaipeng},
journal={arXiv preprint arXiv:2603.23497},
year={2026}
}
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