license: other
license_name: minimax-h3-community-license-agreement
license_link: https://huggingface.co/MiniMaxAI/MiniMax-H3/blob/main/LICENSE
base_model: MiniMaxAI/MiniMax-H3
datasets:
- acvlab/ABot-World-Explorer-500h
tags:
- video
- image-to-video
- world-model
- interactive-world-model
- lora
- diffusion
- action-conditioning
- game-world-model
🎮 H3-World: Turning Language Understanding into World Control
H3-World is the first interactive world model built on MiniMax-H3. Given an initial frame and keyboard controls, it generates action-controlled video with coordinated character and camera motion.
H3-World converts keyboard states into one language instruction per future video latent and uses directed attention routing to bind each instruction to its corresponding latent interval. Using 8,000 gameplay clips from ABot-World-Explorer-500h, it learns 65.6M LoRA parameters, only 0.199% of the 33B MiniMax-H3 backbone.
Checkpoint
| File | Description |
|---|---|
step-10000.safetensors |
Released rank-32 H3-World LoRA checkpoint |
Character controls use W, A, S, and D. Camera controls use I, J, K, and L, with F indicating fast camera motion.
Usage
This checkpoint is a LoRA delta for MiniMax-H3. It requires the MiniMax-H3 base weights and H3-World's directed-attention patch; loading the LoRA through an unmodified MiniMax-H3 pipeline will not reproduce the reported behavior.
Please use the official H3-World code repository for setup, inference, and training instructions.
License
This checkpoint is a LoRA adapter trained from MiniMax-H3 and is treated as a Model Derivative of MiniMax-H3. Use, modification, redistribution, and deployment of the checkpoint and any resulting model are subject to the MiniMax H3 Community License Agreement.
The H3-World source code is separately licensed under the Apache License 2.0. That license applies only to H3-World's independently copyrightable source-code contributions and does not replace, modify, or supersede the MiniMax-H3 license terms applicable to this checkpoint.
Citation
@misc{chen2026h3worldturninglanguageunderstanding,
title={H3-World: Turning Language Understanding into World Control},
author={Danze Chen and Zeqing Wang and Ziyue Lin and Xingyi Yang and Yeying Jin},
year={2026},
eprint={2609.01560},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2609.01560},
}