MODEL PROFILE

DreamX-World-5B

GD-ML · DreamX-World-5B

**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. **DreamX-World-5B** is the long-horizon autoregressive variant of DreamX-World. It is built on top of Wan2.2-TI2V-5B and generates videos from an input image, a text prompt, and keyboard-style camera action commands. Compared with the 5B-Cam variant, DreamX-World-5B uses chunk-wise causal autoregressive inference with KV caching, making long-horizon generation practical, including videos up to about 1 minute at 16 FPS. The model is trained with a scalable data engine on Unreal Engine data, gameplay footage, and real-world videos, together with camera estimation and data filtering. DreamX-World follows a progressive training pipeline for action control, open-ended event response, reinforcement-learning-based action following, and efficient inference through forcing and distillation.

Video generationlicense:mit
Context windowNot disclosed
Input → OutputImage → Video
Model weightsNot verified
Profile updated2026/11/2 19:16:11
OVERVIEW

Model overview

**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. **DreamX-World-5B** is the long-horizon autoregressive variant of DreamX-World. It is built on top of Wan2.2-TI2V-5B and generates videos from an input image, a text prompt, and keyboard-style camera action commands. Compared with the 5B-Cam variant, DreamX-World-5B uses chunk-wise causal autoregressive inference with KV caching, making long-horizon generation practical, including videos up to about 1 minute at 16 FPS. The model is trained with a scalable data engine on Unreal Engine data, gameplay footage, and real-world videos, together with camera estimation and data filtering. DreamX-World follows a progressive training pipeline for action control, open-ended event response, reinforcement-learning-based action following, and efficient inference through forcing and distillation.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Frequently asked questions

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Sources & traceability

许可证:mit。模型介绍与第三方评测均以原来源的具体版本和方法为准。