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Fix model card layout: inline badges and side-by-side demo grids

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  1. README.md +4 -10
README.md CHANGED
@@ -18,9 +18,7 @@ license: apache-2.0
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  **MiniWorld: Democratizing the Training of Video World Models from Scratch**
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- <a href="https://zhao-yian.github.io/MiniWorld/"><img src="https://img.shields.io/badge/Project-Page-1f6feb?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page"></a>
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- <img src="https://img.shields.io/badge/arXiv-Coming%20Soon-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv">
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- <a href="https://github.com/zhao-yian/MiniWorld"><img src="https://img.shields.io/badge/GitHub-Code-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
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  MiniWorld is a minimal and reproducible framework for training streaming video
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  world models from scratch. Instead of adapting a pretrained bidirectional video
@@ -40,13 +38,9 @@ training scripts, and evaluation utilities live in the GitHub repository.
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  Each tile is a 253-frame streaming rollout from the `1B` checkpoint, generated
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  from a single observed frame plus the control signal.
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- <p align="center">
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- <a href="https://huggingface.co/zhaoyian01/MiniWorld/resolve/main/assets/demo_droid.mp4"><img src="assets/demo_droid.webp" width="49%" alt="DROID action-conditioned rollouts"></a>
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- <a href="https://huggingface.co/zhaoyian01/MiniWorld/resolve/main/assets/demo_re10k.mp4"><img src="assets/demo_re10k.webp" width="49%" alt="RealEstate10K camera-conditioned rollouts"></a>
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- </p>
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- <p align="center">
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- <sub><b>DROID</b> action-conditioned rollouts (left) and <b>RealEstate10K</b> camera-conditioned rollouts (right), shown at 2× speed. Click either grid for the full-resolution video, or see the <a href="https://zhao-yian.github.io/MiniWorld/">project page</a> for all 100 rollouts.</sub>
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- </p>
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  ## Model Summary
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  **MiniWorld: Democratizing the Training of Video World Models from Scratch**
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+ <a href="https://zhao-yian.github.io/MiniWorld/"><img src="https://img.shields.io/badge/Project-Page-1f6feb?style=for-the-badge&logo=googlechrome&logoColor=white" alt="Project Page"></a> <img src="https://img.shields.io/badge/arXiv-Coming%20Soon-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white" alt="arXiv"> <a href="https://github.com/zhao-yian/MiniWorld"><img src="https://img.shields.io/badge/GitHub-Code-181717?style=for-the-badge&logo=github&logoColor=white" alt="GitHub"></a>
 
 
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  MiniWorld is a minimal and reproducible framework for training streaming video
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  world models from scratch. Instead of adapting a pretrained bidirectional video
 
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  Each tile is a 253-frame streaming rollout from the `1B` checkpoint, generated
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  from a single observed frame plus the control signal.
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+ <p align="center"><a href="https://huggingface.co/zhaoyian01/MiniWorld/resolve/main/assets/demo_droid.mp4"><img src="assets/demo_droid.webp" width="420" alt="DROID action-conditioned rollouts"></a> <a href="https://huggingface.co/zhaoyian01/MiniWorld/resolve/main/assets/demo_re10k.mp4"><img src="assets/demo_re10k.webp" width="420" alt="RealEstate10K camera-conditioned rollouts"></a></p>
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+
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+ <p align="center"><sub><b>DROID</b> action-conditioned rollouts (left) and <b>RealEstate10K</b> camera-conditioned rollouts (right), shown at 2× speed. Click either grid for the full-resolution video, or see the <a href="https://zhao-yian.github.io/MiniWorld/">project page</a> for all 100 rollouts.</sub></p>
 
 
 
 
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  ## Model Summary
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