DreamX-World is a general-purpose world model for interactive world simulation. It generates diverse, high-fidelity worlds that users can explore, control, and transform with event prompts.

The model is trained with a scalable data engine on Unreal Engine data, gameplay footage, and real-world videos, combined with camera estimation and strict data filtering to learn realistic dynamics and interactions. It follows a progressive training pipeline: learning fine-grained action control first, then open-ended event response, and using Reinforcement Learning to improve action following, interaction consistency, and visual fidelity. Finally, through forcing and distillation, DreamX-World achieves efficient inference, making interactive generation practical at scale.