README.md

---
dataset_info:
- config_name: train
  features:
  - name: video_path 
    dtype: string
  - name: internal_id
    dtype: string  
  - name: prompt
    dtype: string
  - name: url
    dtype: string
  - name: annotation
    struct:
    - name: alignment
      dtype: int64
      range: [1,5] 
    - name: composition  
      dtype: int64
      range: [1,3]
    - name: focus
      dtype: int64
      range: [1,3]
    - name: camera movement
      dtype: int64
      range: [1,3]
    - name: color
      dtype: int64
      range: [1,5]
    - name: lighting accurate
      dtype: int64
      range: [1,4]
    - name: lighting aes
      dtype: int64
      range: [1,5]
    - name: shape at beginning
      dtype: int64
      range: [0,3]
    - name: shape throughout
      dtype: int64
      range: [0,4]
    - name: object motion dynamic
      dtype: int64
      range: [1,5]
    - name: camera motion dynamic
      dtype: int64
      range: [1,5]
    - name: movement smoothness 
      dtype: int64
      range: [0,4]
    - name: movement reality
      dtype: int64
      range: [0,4]
    - name: clear
      dtype: int64
      range: [1,5]
    - name: image quality stability
      dtype: int64
      range: [1,5]
    - name: camera stability
      dtype: int64
      range: [1,3]
    - name: detail refinement
      dtype: int64
      range: [1,5]
    - name: letters
      dtype: int64
      range: [1,4]
    - name: physics law
      dtype: int64
      range: [1,5]
    - name: unsafe type
      dtype: int64
      range: [1,5]
    - name: safety
      dtype: int64
      range: [1,5]
  - name: meta_result
    sequence: 
      dtype: int64
  - name: meta_mask
    sequence:
      dtype: int64
  splits:
  - name: train
    num_examples: 40743

- config_name: regression
  features:
  - name: internal_id
    dtype: string
  - name: prompt
    dtype: string 
  - name: standard_answer
    dtype: string
  - name: video1_path
    dtype: string
  - name: video2_path
    dtype: string
  splits:
  - name: regression
    num_examples: 1795

- config_name: monetbench
  features:
  - name: internal_id
    dtype: string
  - name: prompt
    dtype: string 
  - name: standard_answer
    dtype: string
  - name: video1_path
    dtype: string
  - name: video2_path
    dtype: string
  splits:
  - name: monetbench
    num_examples: 1000

configs:
- config_name: train
  data_files:
  - split: train
    path: train/*.parquet
- config_name: regression
  data_files:
  - split: regression
    path: regression/*.parquet
- config_name: monetbench
  data_files:
  - split: monetbench
    path: monetbench/*.parquet

license: apache-2.0
---

# VisionRewardDB-Video

This dataset is a comprehensive collection of video evaluation data designed for multi-dimensional quality assessment of AI-generated videos. It encompasses annotations across 21 diverse aspects, including text-to-video consistency, aesthetic quality, motion dynamics, physical realism, and technical specifications.  🌟✨
[**Github Repository**](https://github.com/THUDM/VisionReward) 🔗

The dataset is structured to facilitate both model training and standardized evaluation:
- `Train`: A primary training set with detailed multi-dimensional annotations
- `Regression`: A regression set with paired preference data
- `MonetBench`: A benchmark test set for standardized performance evaluation

This holistic approach enables the development and validation of sophisticated video quality assessment models that can evaluate AI-generated videos across multiple critical dimensions, moving beyond simple aesthetic judgments to encompass technical accuracy, semantic consistency, and dynamic performance.


## Annotation Details

Each video in the dataset is annotated with the following attributes:

<table border="1" style="border-collapse: collapse; width: 100%;">
    <tr>
        <th style="padding: 8px; width: 30%;">Dimension</th>
        <th style="padding: 8px; width: 70%;">Attributes</th>
    </tr>
    <tr>
        <td style="padding: 8px;">Alignment</td>
        <td style="padding: 8px;">Alignment</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Composition</td>
        <td style="padding: 8px;">Composition</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Quality</td>
        <td style="padding: 8px;">Color; Lighting Accurate; Lighting Aes; Clear</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Fidelity</td>
        <td style="padding: 8px;">Detail Refinement; Movement Reality; Letters</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Safety</td>
        <td style="padding: 8px;">Safety</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Stability</td>
        <td style="padding: 8px;">Movement Smoothness; Image Quality Stability; Focus; Camera Movement; Camera Stability</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Preservation</td>
        <td style="padding: 8px;">Shape at Beginning; Shape throughout</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Dynamic</td>
        <td style="padding: 8px;">Object Motion dynamic; Camera Motion dynamic</td>
    </tr>
    <tr>
        <td style="padding: 8px;">Physics</td>
        <td style="padding: 8px;">Physics Law</td>
    </tr>
</table>

### Example:  Camera Stability
  - **3:** Very stable
  - **2:** Slight shake 
  - **1:** Heavy shake
- Note: When annotations are missing, the corresponding value will be set to **-1**.

For more detailed annotation guidelines(such as the meanings of different scores and annotation rules), please refer to:

- [annotation_deatils](https://flame-spaghetti-eb9.notion.site/VisioinReward-Video-Annotation-Details-196a0162280e8077b1acef109b3810ff)
- [annotation_deatils_ch](https://flame-spaghetti-eb9.notion.site/VisionReward-Video-196a0162280e80e7806af42fc5808c99)

## Additional Feature Details
The dataset includes two special features: `annotation` and `meta_result`.

### Annotation
The `annotation` feature contains scores across 21 different dimensions of video assessment, with each dimension having its own scoring criteria as detailed above.

### Meta Result
The `meta_result` feature transforms multi-choice questions into a series of binary judgments. For example, for the `Camera Stability` dimension:

| Score | Is the camera very stable? | Is the camera not unstable? |
|-------|--------------------------|---------------------------|
| 3     | 1                        | 1                         |
| 2     | 0                        | 1                         |
| 1     | 0                        | 0                         |

- note: When the corresponding meta_result is -1 (It means missing annotation), the binary judgment should be excluded from consideration

Each element in the binary array represents a yes/no answer to a specific aspect of the assessment. For detailed questions corresponding to these binary judgments, please refer to the meta_qa_en.txt file.

### Meta Mask
The `meta_mask` feature is used for balanced sampling during model training:
- Elements with value 1 indicate that the corresponding binary judgment was used in training
- Elements with value 0 indicate that the corresponding binary judgment was ignored during training

## Data Processing
```bash
cd videos
tar -xvzf train.tar.gz
tar -xvzf regression.tar.gz
tar -xvzf monetbench.tar.gz
```

We provide `extract.py` for processing the `train` dataset into JSONL format. The script can optionally extract the balanced positive/negative QA pairs used in VisionReward training by processing `meta_result` and `meta_mask` fields.

```bash
python extract.py
```

## Citation Information
```
@misc{xu2024visionrewardfinegrainedmultidimensionalhuman,
      title={VisionReward: Fine-Grained Multi-Dimensional Human Preference Learning for Image and Video Generation}, 
      author={Jiazheng Xu and Yu Huang and Jiale Cheng and Yuanming Yang and Jiajun Xu and Yuan Wang and Wenbo Duan and Shen Yang and Qunlin Jin and Shurun Li and Jiayan Teng and Zhuoyi Yang and Wendi Zheng and Xiao Liu and Ming Ding and Xiaohan Zhang and Xiaotao Gu and Shiyu Huang and Minlie Huang and Jie Tang and Yuxiao Dong},
      year={2024},
      eprint={2412.21059},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2412.21059}, 
}
```