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.
DreamX-World enables high-fidelity, controllable exploration across diverse realistic environments, including indoor, urban, natural, and architectural scenes.
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Beyond realistic scenes, DreamX-World also generates fantasy, game-like, sci-fi, and stylized worlds.
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DreamX-World supports both first-person interaction and coherent third-person generation. It keeps camera-follow behavior stable while preserving controllable agent motion and scene consistency.
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DreamX-World supports prompt-driven world events that dynamically change the environment, including flexible and compositional event generation with consistent temporal evolution.
- Single Event: A single event prompt triggers a specific world-changing interaction.
- Compositional Events: Multiple events compose together to create complex, multi-step world transformations.
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This project is licensed under Apache 2.0. See LICENSE for details.
We thank the Wan Team for open-sourcing their code and models.