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.
- Jul 23, 2026: DreamX-World 1.0 is now live! Head over to the Official Website to experience our interactive world model.
- June 15, 2026: We released DreamX-World 1.0 technical report.
- June 15, 2026: We open-sourced DreamX-World-5B that supports 1-min video generation.
- May 11, 2026: We open-sourced DreamX-World-5B-Cam and inference codes.
- ✔️ DreamX-World-5B-Cam Model.
- ✔️ Long-horizon DreamX-World-5B Model.
- ✔️ Release Technical Report.
- DreamX-World-14B-Cam Model.
- Audio-Video Joint Generation Model.
- Install dependencies
pip install -r requirements.txt- Download Wan2.2-5B-TI2V checkpoints from https://huggingface.co/Wan-AI
Please check out inference_README.md for detailed instructions.
| Model | Download Link | Details | Instrutions |
|---|---|---|---|
| DreamX-World-5B-Cam | Huggingface, ModelScope | Bidrectional, Supports 5s Video Generation | inference_README.md |
| DreamX-World-5B | Huggingface, ModelScope | Autoregressive, Supports Long-horizon Video Generation | inference_README.md |
The inference time (shown in the
Costcolumn below) comprises both denoising and VAE decoding time.
| Model | GPUs | Video | Cost (Time in second/Peak Memory) |
|---|---|---|---|
| DreamX-World-5B-Cam | 1xH20 | 5s 720P | 509s/38G |
| DreamX-World-5B | 1xH20 | 5s 720P | 26s/40G |
| DreamX-World-5B | 1xH20 | 60s 720P | 342s/72G |
Note: The demo videos are intentionally compressed to ensure smooth playback, which may result in a slight loss of visual quality.
DreamX-World supports long-horizon autoregressive generation with precise camera control. Progressive training on long rollouts mitigates identity, background, style, and color drift, enabling coherent world exploration over hundreds of frames.
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DreamX-World uses geometry-guided memory retrieval to recover non-local visual evidence from earlier observations. This improves scene persistence when the camera revisits a previously explored region, preserving its layout, object identities, and local appearance.
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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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If you find DreamX-World useful in your research, please consider citing our technical report:
@article{team2026dreamx,
title={DreamX-World 1.0: A General-Purpose Interactive World Model},
author={Team, DreamX and Bai, Yancheng and Chen, Rui and Chu, Xiangxiang and Dang, Rujing and Dou, Hao and Gao, Bingjie and Gu, Qiwen and Hong, Siyu and Lei, Jiachen and others},
journal={arXiv preprint arXiv:2606.16993},
year={2026}
}This project is licensed under Apache 2.0. See LICENSE for details.
We thank the Wan Team for open-sourcing their code and models.