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ForgeWM/ForgeWM-data

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… See the full description on the dataset page: https://huggingface.co/datasets/ForgeWM/ForgeWM-data.

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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

bash
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

FieldValue
Clips4000*10 = 40000
Shards10 LMDB files
Latent shape(21, 16, 44, 80) — Wan2.1 VAE, 84 frames @ 352×640 decoded
Keyboardone-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

bibtex
@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):

bibtex
@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.