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This repository contains the implementation of the following paper:
> **VBench: Comprehensive Benchmark Suite for Video Generative Models**<br>
> [Ziqi Huang](https://ziqihuangg.github.io/)<sup>∗</sup>, [Yinan He](https://github.com/yinanhe)<sup>∗</sup>, [Jiashuo Yu](https://scholar.google.com/citations?user=iH0Aq0YAAAAJ&hl=zh-CN)<sup>∗</sup>, [Fan Zhang](https://github.com/zhangfan-p)<sup>∗</sup>, [Chenyang Si](https://chenyangsi.top/), [Yuming Jiang](https://yumingj.github.io/), [Yuanhan Zhang](https://zhangyuanhan-ai.github.io/),  [Tianxing Wu](https://tianxingwu.github.io/), [Qingyang Jin](https://github.com/Vchitect/VBench), [Nattapol Chanpaisit](https://nattapolchan.github.io/me), [Yaohui Wang](https://wyhsirius.github.io/), [Xinyuan Chen](https://scholar.google.com/citations?user=3fWSC8YAAAAJ), [Limin Wang](https://wanglimin.github.io), [Dahua Lin](http://dahua.site/)<sup>+</sup>, [Yu Qiao](http://mmlab.siat.ac.cn/yuqiao/index.html)<sup>+</sup>, [Ziwei Liu](https://liuziwei7.github.io/)<sup>+</sup><br>
> IEEE/CVF Conference on Computer Vision and Pattern Recognition (**CVPR**), 2024



## :fire: Updates
- [03/2024] :fire::fire: **[VBench-Reliability](https://github.com/Vchitect/VBench/tree/master/vbench2_beta_reliability)** :fire::fire: We now support evaluating the **reliability** (*e.g.*, culture, fairness, bias, safety) of video generative models.
- [03/2024] :fire::fire: **[VBench-I2V](https://github.com/Vchitect/VBench/tree/master/vbench2_beta_i2v)** :fire::fire: We now support evaluating **Image-to-Video (I2V)** models. We also provide [Image Suite](https://drive.google.com/drive/folders/1fdOZKQ7HWZtgutCKKA7CMzOhMFUGv4Zx?usp=sharing).
- [03/2024] We support **evaluating customized videos**! See [here](https://github.com/Vchitect/VBench/?tab=readme-ov-file#new-evaluate-your-own-videos) for instructions.
- [01/2024] PyPI pacakge is released! [![PyPI](https://img.shields.io/pypi/v/vbench)](https://pypi.org/project/vbench/). Simply `pip install vbench`.
- [12/2023] :fire::fire: **[VBench](https://github.com/Vchitect/VBench?tab=readme-ov-file#usage)** :fire::fire: Evaluation code released for 16 **Text-to-Video (T2V) evaluation** dimensions. 
    - `['subject_consistency', 'background_consistency', 'temporal_flickering', 'motion_smoothness', 'dynamic_degree', 'aesthetic_quality', 'imaging_quality', 'object_class', 'multiple_objects', 'human_action', 'color', 'spatial_relationship', 'scene', 'temporal_style', 'appearance_style', 'overall_consistency']`
- [11/2023] Prompt Suites released. (See prompt lists [here](https://github.com/Vchitect/VBench/tree/master/prompts))
  

## :mega: Overview
![overall_structure](./asset/fig_teaser_new.jpg)
We propose **VBench**, a comprehensive benchmark suite for video generative models. We design a comprehensive and hierarchical <b>Evaluation Dimension Suite</b> to decompose "video generation quality" into multiple well-defined dimensions to facilitate fine-grained and objective evaluation. For each dimension and each content category, we carefully design a <b>Prompt Suite</b> as test cases, and sample <b>Generated Videos</b> from a set of video generation models. For each evaluation dimension, we specifically design an <b>Evaluation Method Suite</b>, which uses carefully crafted method or designated pipeline for automatic objective evaluation. We also conduct <b>Human Preference Annotation</b> for the generated videos for each dimension, and show that VBench evaluation results are <b>well aligned with human perceptions</b>. VBench can provide valuable insights from multiple perspectives.


## :mortar_board: Evaluation Results
<p align="center">
  <img src="./asset/radar-open.jpg" width="48%" style="margin-right: 4%;" />
  <img src="./asset/radar-close.jpg" width="48%" />
</p>

We visualize VBench evaluation results of various publicly available video generation models, as well as Gen-2 and Pika, across 16 VBench dimensions. We normalize the results per dimension for clearer comparisons. (See numeric values at our [Leaderboard](https://huggingface.co/spaces/Vchitect/VBench_Leaderboard))

<!-- The values have been normalized for better readability of the chart. The normalization process involves scaling each set of performance values to a common scale between 0.3 and 0.8. The formula used for normalization is: (value - min value) / (max value - min value). -->


## :hammer: Installation
### Install with pip
```
pip install vbench
```

To evaluate some video generation ability aspects, you need to install [detectron2](https://github.com/facebookresearch/detectron2) via:
   ```
   pip install detectron2@git+https://github.com/facebookresearch/detectron2.git
   ```
    
If there is an error during [detectron2](https://github.com/facebookresearch/detectron2) installation, see [here](https://detectron2.readthedocs.io/en/latest/tutorials/install.html).

Download [VBench_full_info.json](https://github.com/Vchitect/VBench/blob/master/vbench/VBench_full_info.json) to your running directory to read the benchmark prompt suites.

### Install with git clone
    git clone https://github.com/Vchitect/VBench.git
    pip install -r VBench/requirements.txt
    pip install VBench
    
If there is an error during [detectron2](https://github.com/facebookresearch/detectron2) installation, see [here](https://detectron2.readthedocs.io/en/latest/tutorials/install.html).

## Usage
Use VBench to evaluate videos, and video generative models.
- A Side Note: VBench is designed for evaluating different models on a standard benchmark. Therefore, by default, we enforce evaluation on the **standard VBench prompt lists** to ensure **fair comparisons** among different video generation models. That's also why we give warnings when a required video is not found. This is done via defining the set of prompts in [VBench_full_info.json](https://github.com/Vchitect/VBench/blob/master/vbench/VBench_full_info.json). However, we understand that many users would like to use VBench to evaluate their own videos, or videos generated from prompts that does not belong to the VBench Prompt Suite, so we also added the function of **Evaluating Your Own Videos**. Simply turn the `custom_input` flag on, and you can evaluate your own videos.


### **[New]** Evaluate Your Own Videos
We support evaluating any video. Simply provide the path to the video file, or the path to the folder that contains your videos. There is no requirement on the videos' names.
- Note: We support customized videos / prompts for the following dimensions: `'subject_consistency', 'background_consistency', 'motion_smoothness', 'dynamic_degree', 'aesthetic_quality', 'imaging_quality'`


To evaluate videos with customed input prompt, run our script with the `custom_input` flag on:
```
python evaluate.py \
    --dimension $DIMENSION \
    --videos_path /path/to/folder_or_video/ \
    --custom_input
```
alternatively you can use our command:
```
vbench evaluate \
    --dimension $DIMENSION \
    --videos_path /path/to/folder_or_video/ \
    --custom_input
```

### Evaluation on the Standard Prompt Suite of VBench

##### command line 
```bash
    vbench evaluate --videos_path $VIDEO_PATH --dimension $DIMENSION
```
For example:
```bash
    vbench evaluate --videos_path "sampled_videos/lavie/human_action" --dimension "human_action"
```
##### python
```python
    from vbench import VBench
    my_VBench = VBench(device, <path/to/VBench_full_info.json>, <path/to/save/dir>)
    my_VBench.evaluate(
        videos_path = <video_path>,
        name = <name>,
        dimension_list = [<dimension>, <dimension>, ...],
    )
```
For example: 
```python
    from vbench import VBench
    my_VBench = VBench(device, "vbench/VBench_full_info.json", "evaluation_results")
    my_VBench.evaluate(
        videos_path = "sampled_videos/lavie/human_action",
        name = "lavie_human_action",
        dimension_list = ["human_action"],
    )
```

### Example of Evaluating VideoCrafter-1.0
We have provided scripts to download VideoCrafter-1.0 samples, and the corresponding evaluation scripts.
```
# download sampled videos
sh scripts/download_videocrafter1.sh

# evaluate VideoCrafter-1.0
sh scripts/evaluate_videocrafter1.sh
```



## :gem: Pre-Trained Models
[Optional] Please download the pre-trained weights according to the guidance in the `model_path.txt` file for each model in the `pretrained` folder to `~/.cache/vbench`.

## :bookmark_tabs: Prompt Suite

We provide prompt lists are at `prompts/`. 

Check out [details of prompt suites](https://github.com/Vchitect/VBench/tree/master/prompts), and instructions for [**how to sample videos for evaluation**](https://github.com/Vchitect/VBench/tree/master/prompts).

## :surfer: Evaluation Method Suite

To perform evaluation on one dimension, run this:
```
python evaluate.py --videos_path $VIDEOS_PATH --dimension $DIMENSION
```
- The complete list of dimensions:
    ```
    ['subject_consistency', 'background_consistency', 'temporal_flickering', 'motion_smoothness', 'dynamic_degree', 'aesthetic_quality', 'imaging_quality', 'object_class', 'multiple_objects', 'human_action', 'color', 'spatial_relationship', 'scene', 'temporal_style', 'appearance_style', 'overall_consistency']
    ```

Alternatively, you can evaluate multiple models and multiple dimensions using this script:
```
bash evaluate.sh
```
- The default sampled video paths:
    ```
    vbench_videos/{model}/{dimension}/{prompt}-{index}.mp4/gif
    ```

To filter static videos in the temporal flickering dimension, run this:
```
python static_filter.py --videos_path $VIDEOS_PATH
```


## :black_nib: Citation

   If you find our repo useful for your research, please consider citing our paper:

   ```bibtex
    @InProceedings{huang2023vbench,
        title={{VBench}: Comprehensive Benchmark Suite for Video Generative Models},
        author={Huang, Ziqi and He, Yinan and Yu, Jiashuo and Zhang, Fan and Si, Chenyang and Jiang, Yuming and Zhang, Yuanhan and Wu, Tianxing and Jin, Qingyang and Chanpaisit, Nattapol and Wang, Yaohui and Chen, Xinyuan and Wang, Limin and Lin, Dahua and Qiao, Yu and Liu, Ziwei},
        booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
        year={2024}
    }
   ```


## :hearts: Acknowledgement

### VBench Contributors
Order is based on the time joining the project: 
> [Ziqi Huang](https://ziqihuangg.github.io/), [Yinan He](https://github.com/yinanhe), [Jiashuo Yu](https://scholar.google.com/citations?user=iH0Aq0YAAAAJ&hl=zh-CN), [Fan Zhang](https://github.com/zhangfan-p), [Nattapol Chanpaisit](https://nattapolchan.github.io/me), [Xiaojie Xu](https://github.com/xjxu21).

### Open-Sourced Repositories
This project wouldn't be possible without the following open-sourced repositories:
[AMT](https://github.com/MCG-NKU/AMT/), [UMT](https://github.com/OpenGVLab/unmasked_teacher), [RAM](https://github.com/xinyu1205/recognize-anything), [CLIP](https://github.com/openai/CLIP), [RAFT](https://github.com/princeton-vl/RAFT), [GRiT](https://github.com/JialianW/GRiT), [IQA-PyTorch](https://github.com/chaofengc/IQA-PyTorch/), [ViCLIP](https://github.com/OpenGVLab/InternVideo/tree/main/Data/InternVid), and [LAION Aesthetic Predictor](https://github.com/LAION-AI/aesthetic-predictor).