Dual license: CC BY-NC 4.0 data, Apache-2.0 code
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README.md
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# VBVR-Pro-Bench
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<p align="center">
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video" target="_blank">
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<img alt="
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</a>
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Image" target="_blank">
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<img alt="
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</a>
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-RL" target="_blank">
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<a href="https://
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-Bench/blob/main/
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<img alt="License: CC BY-NC 4.0" src="https://img.shields.io/badge/License-CC_BY--NC_4.0-blue.svg" height="20" />
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</a>
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</p>
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Generate one video (or image sequence) per instance from `first_frame.png` + `prompt.txt`, then score with [VBVR-Pro-Bench](https://github.com/Video-Reason/VBVR-Pro-Bench).
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> This benchmark is **1024 × 1024**, as generated. The training splits
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> ([SFT-Video](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video),
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> [SFT-Image](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Image))
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# VBVR-Pro-Bench
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<p align="center">
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<a href="https://video-reason.com/?v=pro" target="_blank">
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<img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
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</a>
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<a href="https://huggingface.co/papers/2602.20159" target="_blank">
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<img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR_Pro-red?logo=arxiv" height="20" />
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</a>
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<a href="https://github.com/Video-Reason/VBVR-Pro" target="_blank">
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<img alt="Code" src="https://img.shields.io/badge/Training_&_Inference-VBVR_Pro-100000?style=flat-square&logo=github&logoColor=white" height="20" />
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</a>
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<a href="https://github.com/Video-Reason/VBVR-Pro-Bench" target="_blank">
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<img alt="Eval Code" src="https://img.shields.io/badge/Evaluation_code-VBVR_Pro_Bench-100000?style=flat-square&logo=github&logoColor=white" height="20" />
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</a>
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video" target="_blank">
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<img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Dataset-Video-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Image" target="_blank">
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<img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Dataset-Image-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-RL" target="_blank">
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<img alt="Dataset" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Dataset-RL-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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<a href="https://video-reason.com/pro/bench/#leaderboard" target="_blank">
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<img alt="Leaderboard" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Bench-Leaderboard-ffc107?color=ffc107&logoColor=white" height="20" />
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</a>
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<a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-Bench/blob/main/LICENSE.md" target="_blank">
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<img alt="License: CC BY-NC 4.0" src="https://img.shields.io/badge/License-CC_BY--NC_4.0-blue.svg" height="20" />
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</a>
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</p>
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Generate one video (or image sequence) per instance from `first_frame.png` + `prompt.txt`, then score with [VBVR-Pro-Bench](https://github.com/Video-Reason/VBVR-Pro-Bench).
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For the full project — training data, recipes and models — see [VBVR-Pro](https://github.com/Video-Reason/VBVR-Pro).
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> This benchmark is **1024 × 1024**, as generated. The training splits
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> ([SFT-Video](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video),
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> [SFT-Image](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Image))
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