H3-World: Turning Language Understanding into World Control

2026-09-01

AI Summary

该研究通过将大型视频生成器MiniMax-H3与自然语言指令结合，开发出H3-World框架，实现了对虚拟世界中角色和摄像机的精准时间控制。

Abstract

We present H3-World, an efficient framework that turns the 33B MiniMax-H3 video generator into an interactive world model. Our key finding is that, as large video generators become more capable, language is emerging as a natural interface for control. MiniMax-H3, for example, already supports zero-shot control of character behavior and camera motion through natural-language instructions. Building on this, H3-World turns this coarse language interface into precise, temporally grounded world control, without introducing dedicated action modules. Specifically, we represent each action as a structured combination of character and camera instructions, and align them with the corresponding temporal video latents. To make the control temporally precise, we further introduce temporal attention routing, which restricts each instruction to its intended time interval and reduces control leakage across actions. Importantly, H3-World directly reuses the semantic representations learned during large-scale video pretraining and requires only lightweight adaptation. With only 8,000 gameplay samples, 10,000 LoRA optimization steps, and 0.199% trainable parameters, H3-World achieves effective character and camera control while preserving strong generation quality. It also generalizes to unseen scenarios. These results show that the control capabilities emerging in large video generators can be efficiently transformed into interactive world control.

Problem

• language understanding
• world control
• video generator
• natural language instructions
• temporally precise

Innovation

• H3-World
• temporal attention routing
• language-based control
• interactive world model
• lightweight adaptation