---
pretty_name: GraftSet-Video
language:
  - en
  - zh
size_categories:
  - 1K<n<10K
task_categories:
  - text-to-video
tags:
  - video
  - text-to-video
  - video-generation
  - reinforcement-learning
  - reward-optimization
  - synthetic-data
  - graftood
license: apache-2.0
---

# Introduction

GraftSet-Video is the external video exemplar dataset introduced in **GraftOOD: Grafting Out-of-Distribution Exemplars into Advantage-Weighted Rectification for Video Reward Optimization**. It provides prompt-matched video sequences for augmenting online video reward optimization with direct exemplar supervision.

## Dataset overview

| Property | Value |
|---|---|
| Videos | 9,588 |
| Unique prompts | 2,397 |
| Videos per prompt | 4 |
| Source model identifiers | 9 |
| Video resolution | 832 × 480 pixels (width × height) |
| Frames per video | 81 |
| Frame rate | 16 fps |
| Stored reward | HPSv3 score |
| Mean / standard deviation of reward | 9.735 / 1.413 |

### Source composition

`model_name` records the video generator identifier. 

| `model_name` | Videos |
|---|---:|
| `wan2_2` | 2,164 |
| `vidu_q2` | 1,163 |
| `seedance_1_5` | 1,144 |
| `kling_2_5` | 1,098 |
| `kling3` | 915 |
| `seedance2` | 911 |
| `veo3_1` | 883 |
| `hailuo` | 830 |
| `sora2` | 480 |
| **Total** | **9,588** |

## Repository structure

```text
.
├── README.md
├── metadata.jsonl
└── videos/
    ├── hailuo/
    ├── kling3/
    ├── kling_2_5/
    ├── seedance2/
    ├── seedance_1_5/
    ├── sora2/
    ├── veo3_1/
    ├── vidu_q2/
    └── wan2_2/
        └── 1_wan2_2.mp4
```

## Metadata schema

| Field | Type | Description |
|---|---|---|
| `file_name` | string | Video path relative to the directory containing `metadata.jsonl`, such as `videos/wan2_2/1_wan2_2.mp4`. |
| `prompt_id` | string | Shared identifier for the four videos belonging to one prompt. |
| `prompt` | string | Original text prompt paired with the video. |
| `model_name` | string | Source generator identifier. |
| `reward` | float | Original offline HPSv3 score, Higher values indicate higher scores under this reward model. |

Example record, formatted across multiple lines for readability:

```json
{
  "file_name": "videos/wan2_2/1_wan2_2.mp4",
  "prompt_id": "398b3a665bd230e5",
  "prompt": "The camera starts on a vibrant, diamond-shaped kite dancing against a brilliant blue sky. A slow tilt down reveals a young daughter in a sun-hat and a pink dress, her face lit with joy as she expertly handles the kite string in a sprawling green park. Her laughter rings out as the kite soars higher. Later, the scene shifts to her sitting on a picnic blanket with her family. The camera focuses on a close-up of hands cutting a large, juicy watermelon into perfect triangular slices, ready to be shared on this perfect summer day.",
  "model_name": "wan2_2",
  "reward": 9.292619972229003
}
```



## Loading the dataset

Run the following from the dataset repository root. It uses only the Python standard library:

```python
import json
from collections import defaultdict
from pathlib import Path

root = Path.cwd()
with (root / "metadata.jsonl").open(encoding="utf-8") as f:
    records = [json.loads(line) for line in f if line.strip()]

by_prompt = defaultdict(list)
for record in records:
    video_path = root / record["file_name"]
    assert video_path.is_file(), video_path
    by_prompt[record["prompt_id"]].append(record)

assert len(records) == 9588
assert len(by_prompt) == 2397
assert all(len(group) == 4 for group in by_prompt.values())

example = records[0]
print(example["prompt"])
print(root / example["file_name"])
print(example["reward"])
```

### Load with Hugging Face Datasets

Install the library:

```bash
pip install -U datasets
```

From the dataset repository root:

```python
from pathlib import Path
from datasets import Video, load_dataset

dataset = load_dataset(
    "videofolder",
    data_dir=str(Path.cwd()),
    split="train",
    drop_labels=True,
)
dataset = dataset.cast_column("video", Video(decode=False))
print(dataset[0])
```

VideoFolder associates `file_name` with a `video` feature and retains the additional metadata columns. `decode=False` returns the underlying video reference without decoding frames. For decoded video access, follow the [Hugging Face video-loading documentation](https://huggingface.co/docs/datasets/video_load) and install its required decoder dependencies.

Once this repository is published on the Hub, replace `YOUR_NAMESPACE/GraftSet-Video` below with its actual repository ID:

```python
from datasets import Video, load_dataset

dataset = load_dataset(
    "YOUR_NAMESPACE/GraftSet-Video",
    split="train",
    drop_labels=True,
)
dataset = dataset.cast_column("video", Video(decode=False))
```



## Paper and citation

If you find this project useful, please consider citing:

```
@article{fu2026graftood,
  title={GraftOOD: Grafting Out-of-Distribution Exemplars into Advantage-Weighted Rectification for Video Reward Optimization},
  author={Fu, Zheming and He, Ruizhe and Ning, Wenbin and Ma, Xiaoxiao and Yang, Xuan and Mu, Lianrui and Huang, Haoyang and Liu, Chang and Wang, Lei and Fu, Siming},
  journal={arXiv preprint arXiv:2610.13470},
  year={2026}
}
@article{fu2026videoawr,
  title={VideoAWR: Reinforcing Video Flow Models with Decoupled-Timestep Advantage-Weighted Rectification},
  author={Fu, Zheming and Ning, Wenbin and He, Ruizhe and Shang, Wei and Ma, Xiaoxiao and Mu, Lianrui and Liu, Chang and Wang, Lei and Fu, Siming},
  journal={arXiv preprint arXiv:2610.13338},
  year={2026}
}
@article{fu2026flowawr,
  title={FlowAWR: Online Adaptive Flow Reinforcement via Advantage-Weighted Rectification},
  author={Fu, Zheming and He, Ruizhe and Shang, Wei and Ma, Xiaoxiao and Wang, Lei and Liu, Chang and Fu, Siming},
  journal={arXiv preprint arXiv:2606.30376},
  year={2026}
}
```

## Acknowledgments

We acknowledge the authors and maintainers of the source video reward-model resources, the video generation systems represented in this release, and HPSv3. Full bibliographic references for these resources are provided in the accompanying paper.