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README.md

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 ---
-license: apache-2.0
+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
 ---
+
+# GraftSet-Video
+
+**9,588 generated video exemplars · 2,397 prompts · 4 videos per prompt · 9 source models**
+
+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 | Exactly 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
+
+`metadata.jsonl` is UTF-8 JSON Lines: one JSON object per video.
+
+| 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
+
+### Read a local copy without decoding videos
+
+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}
+}
+```
+
+## 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.

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