Datasets:

Video-Reason
/

VBVR-Pro-SFT-Video

---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 1M<n<10M
task_categories:
- text-to-video
pretty_name: VBVR-Pro-SFT-Video
tags:
- video-generation
- image-to-video
- visual-reasoning
- training
configs:
- config_name: preview
  default: true
  data_files:
  - split: train
    path: preview/video.parquet
---

# VBVR-Pro-SFT-Video

<div align="center">

<p align="center">
    <a href="https://video-reason.com/?v=pro" target="_blank">
        <img alt="Project Page" src="https://img.shields.io/badge/Project%20-%20Homepage-4285F4" height="20" />
    </a>
    <a href="https://arxiv.org/abs/2608.26105" target="_blank">
        <img alt="arXiv" src="https://img.shields.io/badge/arXiv-VBVR_Pro-red?logo=arxiv" height="20" />
    </a>
    <a href="https://github.com/Video-Reason/VBVR-Pro" target="_blank">
        <img alt="Code" src="https://img.shields.io/badge/Training_&_Inference-VBVR_Pro-100000?style=flat-square&logo=github&logoColor=white" height="20" />
    </a>
    <a href="https://github.com/Video-Reason/VBVR-Pro-Bench" target="_blank">
        <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" />
    </a>
    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video" target="_blank">
        <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" />
    </a>
    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Image" target="_blank">
        <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" />
    </a>
    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-RL" target="_blank">
        <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" />
    </a>
    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-Bench" target="_blank">
        <img alt="Bench Data" src="https://img.shields.io/badge/%F0%9F%A4%97%20_VBVR_Pro_Bench-Data-ffc107?color=ffc107&logoColor=white" height="20" />
    </a>
    <a href="https://video-reason.com/pro/bench/#leaderboard" target="_blank">
        <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" />
    </a>
    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video/blob/main/LICENSE.md#code--apache-license-20">
        <img alt="Code License" src="https://img.shields.io/badge/Code-Apache_2.0-blue.svg" height="20" />
    </a>
    <a href="https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video/blob/main/LICENSE.md#data-and-benchmark-materials--cc-by-nc-40">
        <img alt="Data License" src="https://img.shields.io/badge/Data-CC_BY--NC_4.0-blue.svg" height="20" />
    </a>
</p>

</div>

The **video (I2V) supervised-fine-tuning split** of VBVR-Pro: 1.24M programmatically generated reasoning instances across 250 parameterized tasks, one tar.gz per task.


## At a glance

| Property | Value |
|---|---|
| Tasks | **250** |
| Instances | **1,250,000** (5,000 per task) |
| Archives | 250 tar.gz, one per task |
| Total size | **76.6 GB** |
| Resolution | **512 × 512** |
| Video codec | H.264, CRF 20 |

## Layout

```
.
├── tars/
│   ├── G-11_handle_object_reappearance_data-generator.tar.gz
│   └── …                                     # 250 archives, one per task
├── jsonl/
│   ├── G-11_handle_object_reappearance_data-generator.jsonl
│   └── …                                     # 250 files, one per task
└── meta_video_train.json                     # index over the 250 tasks
```

Each archive holds one task and extracts to:

```
G-11_handle_object_reappearance_data-generator/                     # task
└── handle_object_reappearance_task/                                # subtask
    ├── handle_object_reappearance_00000000/                        # sample
    │   ├── first_frame.png          # conditioning image  (512 × 512)
    │   ├── metadata.json            # task parameters, ground truth, scoring contract
    │   └── video/
    │       ├── prompt.txt           # instruction
    │       ├── ground_truth.mp4     # reference video     (16 fps, H.264)
    │       └── final_frame.png      # last frame of the reference video
    ├── handle_object_reappearance_00000001/                        # same files
    ├── …
    └── handle_object_reappearance_00004999/                        # 5,000 samples per task
```

Sample ids run `00000000`–`00004999`.

All 250 archives share this shape and no two overlap, so they can be extracted
into one directory.

### Index files

`meta_video_train.json` maps every task to its jsonl:

```json
{
  "G-11_handle_object_reappearance_data-generator": {
    "root": ".",
    "annotation": "jsonl/G-11_handle_object_reappearance_data-generator.jsonl",
    "length": 5000,
    "task": "Video-SFT"
  }
}
```

`root` is the directory the archives were extracted into; `length` is that task's
row count. Each jsonl row points at one sample, with every path relative to `root`:

| Field | Meaning |
|---|---|
| `sample_id` | e.g. `handle_object_reappearance_00000000` |
| `first_frame` | conditioning image |
| `metadata` | that sample's `metadata.json` |
| `clip_path` | reference video |
| `final_frame` | last frame of the reference video |
| `prompt` | instruction |

## Usage

```bash
huggingface-cli download Video-Reason/VBVR-Pro-SFT-Image --repo-type dataset --local-dir data/VBVR-Pro-SFT-Image
huggingface-cli download Video-Reason/VBVR-Pro-SFT-Video --repo-type dataset --local-dir data/VBVR-Pro-SFT-Video
```

The [VBVR-Pro training code](https://github.com/Video-Reason/VBVR-Pro) unpacks the
archives and emits the per-trainer manifests in one step:

```bash
python training/prepare_data.py \
  --image-archives data/VBVR-Pro-SFT-Image \
  --video-archives data/VBVR-Pro-SFT-Video \
  --output-dir data/prepared
```

To use the data directly instead, extract every archive into one directory and
point `root` at it:

```bash
mkdir -p data/extracted
for f in data/VBVR-Pro-SFT-Video/tars/*.tar.gz; do tar xzf "$f" -C data/extracted; done
```

## License

VBVR-Pro source code, scripts, configuration files and task-specific scoring
software — including everything in this repository — are licensed under the
Apache License 2.0. VBVR-Pro data and benchmark materials are separately
licensed under CC BY-NC 4.0. Model weights and third-party materials remain
subject to their applicable model-card and upstream terms. See
[LICENSE.md](https://huggingface.co/datasets/Video-Reason/VBVR-Pro-SFT-Video/blob/main/LICENSE.md) for details.

## Citation

```bibtex
@misc{xu2026vbvrproscalableverifiablesuite,
      title={VBVR-Pro: A Scalable and Verifiable Suite for Native Visual Reasoning},
      author={Junxiang Xu and Ruisi Wang and Fanyi Pu and Maijunxian Wang and Ran Ji and Tongxi Zhou and Chenyang Gu and Jing Zuo and Hongcan Xiao and Yimeng Geng and Wanqi Yin and Wei Chen and Oscar Qian and Zhengan Yan and Ziqi Huang and Haiwen Diao and Liang Pan and Bo Li and Xiangyu Fan and Dezhi Luo and Fengyuan Yu and Zehong Zhao and Qingying Gao and Tinghui Zhu and Yilan Zhang and Jingqi Tong and Pinyuan Feng and Zhengze Jiang and Letian Wang and Ziyu Guo and Renrui Zhang and Jieneng Chen and Sonia Joseph and Constantin Venhoff and Saman Motamed and Mengyue Yang and Chandra Sripada and Alan Yuille and Philip Torr and Lvmin Zhang and Vikash Kumar and Daniel Khashabi and Nikolaus Kriegeskorte and Raphaël Millière and Vincent C. Müller and Anyi Rao and Quan Wang and Ziwei Liu and Dahua Lin and Lei Yang and Hokin Deng and Zhongang Cai},
      year={2026},
      eprint={2608.26105},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2608.26105},
}
```