H3-World: Turning Language Understanding into World Control
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H3-World is a system that translates language understanding into world control by composing character and camera actions into textual instructions, which are then injected through MiniMax-H3’s pretrained text pathway. It demonstrates efficiency and generalizability, achieving controllable character and camera motion with only 8,000 gameplay samples, 10,000 LoRA steps, and 0.199% trainable parameters. This allows for unseen action compositions and visual scenarios. Resources include an ArXiv paper, code on GitHub, a project page, and a Hugging Face model.
- Language-Native Control: Composes character and camera actions into textual instructions and injects them through MiniMax-H3’s pretrained text pathway.
- Temporally Grounded: Assigns one action prompt to each video latent interval, enabling precise control when actions change over time.
- Efficient & Generalizable: Uses only 8,000 gameplay samples, 10,000 LoRA steps, and 0.199% trainable parameters to achieve controllable character and camera motion, including unseen action compositions and visual scenarios.
📄 ArXiv: https://arxiv.org/abs/2609.01560 💻 Code: https://github.com/Danzer1xxxxChan/H3-World 🏠 Project: https://danzer1xxxxchan.github.io/H3-World/ 🤗 Model: https://huggingface.co/DANNY621/H3-World