Datasets:
metadata
license: apache-2.0
size_categories:
- 10K<n<100K
pretty_name: ForgeWM Training Data
tags:
- world-model
- minecraft
- video-diffusion
- action-conditioned
task_categories:
- image-to-video
ForgeWM Training Data
Pre-encoded latents for training ForgeWM (arXiv:2608.14022).
Project Page: https://asdfo123.github.io/ForgeWM/
This is a re-packaging of the GameFactory GF-Minecraft dataset, encoded into Wan2.1 VAE latents and sharded as LMDB for direct use by ForgeWM training scripts. The underlying gameplay videos are from GameFactory — we just made them training-ready.
Quick Start
huggingface-cli download ForgeWM/ForgeWM-data \
--local-dir ./data/action_lmdb --repo-type dataset
Then point ForgeWM configs to ./data/action_lmdb.
What's Inside
| Field | Value |
|---|---|
| Clips | 4000*10 = 40000 |
| Shards | 10 LMDB files |
| Latent shape | (21, 16, 44, 80) — Wan2.1 VAE, 84 frames @ 352×640 decoded |
| Keyboard | one-hot W/S/A/D |
| Mouse | [yaw, pitch], normalized |
| Total size | ~89 GB |
Processing note: GameFactory's pitch convention (+pitch = look-down) is
flipped to MG2's (mouse[0] > 0 = look-up) at encoding time.
Citation
@misc{li2026forgewm,
title = {ForgeWM: Progressive Causal Training for Few-Step
Action-Conditioned Video World Models},
author = {Xinye Li and Lingshuai Lin and Lei Wang and Liuzhou Zhang and
Jialin Cui and Qingshan Li and Guanchu Wang and Qingbin Liu and
Xi Chen and Jiang Bian and Wai Lam},
year = {2026},
eprint = {2608.14022},
archivePrefix = {arXiv},
primaryClass = {cs.CV},
url = {https://arxiv.org/abs/2608.14022}
}
Please also cite GameFactory (the underlying data):
@misc{yu2024gamefactory,
title={GameFactory: Creating New Games with Generative Interactive Videos},
author={Yu, Jiwen and Qin, Yiran and Wang, Xintao and Wan, Pengfei and Zhang, Di and Liu, Xihui},
year={2025},
eprint={2501.08325},
archivePrefix={arXiv},
}
License
Apache 2.0 for this repackaging. The underlying GF-Minecraft data follows GameFactory's license — please consult their terms before redistribution.