Competitions

We have two tracks for video generation: (1) short videos, and (2) long videos.

More information on the competition will be announced at the official competition site.

Submission Requirement

1. Prompt List

Provide one generated video based on each text prompt.

Short Videos: Sample videos from competitions/short_prompt_list.txt Long Videos: Sample videos from competitions/long_prompt_list.txt

2. Video Requirement

The table below is the sampling requirement.

Video Type	Prompt Count	Resolution	Duration	Frame Rate	Frame Count
Short Videos	200	No Limit	1.6-4.0s	8-24 FPS	16-96
Long Videos	40	No Limit	10.0-40.0s	8-24 FPS	-

Note:

For short videos, must satisfy the requirements on Duration, Frame Rate, and Frame Count at the same time.
For long videos, there is no additional requirement on Frame Count, as long as the sampled videos satisfy the requirements on Duration and Frame Rate at the same time.

3. File Structure Requirements

Please organize generated videos according to the following requirements.

PNG requirement: All submissions must be in png format. During submission, the teams are required to indicate the Frame Rate in terms of FPS.
File structure: The png frames of the same video should be saved in the same sub-folder. One sub-folder for one sampled video.
Frame name: Name the png frames according to the frame order, in 5 digits, zero-filled. The first frame would is 00000.png.
Folder name: For each prompt, use the prompt index followed by the first three words of the prompt.
- prompt index: 4 digits, zero-filled, start with 0001.
- the first three words of the prompt: case sensitive, and replace space with underscore _.

Your submission folder should look like this:

YOUR-TEAM-NAME_videos.zip
├── Short_Videos
│   ├── 0001_Close_up_of     # folder name corresponds to each prompt
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
│   ├── 0002_Turtle_swimming_in     
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
│   ├── ...
│   ├── 0200_cruise_ship_in
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
├── Long_Videos
│   ├── 0001_A_stylish_woman     
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
│   ├── 0002_Several_giant_wooly
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
│   ├── ...
│   ├── 0040_The_Glenfinnan_Viaduct
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...

Pseudo-Code for Sampling

This is for your reference only. You can choose to save MP4 first before converting to PNG.

1. Sampling and save to MP4

type_list = ['short_prompt_list', 'long_prompt_list']

for prompt_type in type_list:

    # set random seed
    if args.seed:
        torch.manual_seed(args.seed)    
    
    # read prompt list
    with open(f'./{prompt_type}.txt', 'r') as f:
        prompt_list = f.readlines()
    prompt_list = [prompt.strip() for prompt in prompt_list]
    
    for index, prompt in enumerate(prompt_list):

        # perform sampling
        video = sample_func(prompt)
        cur_save_path = f'{args.save_path}/{video-name}.mp4'
        torchvision.io.write_video(cur_save_path, video, fps=fps)

2. Convert MP4 to PNG

ffmpeg

ffmpeg -i input_video.mp4 -start_number 0 ./%05d.png

PIL

from PIL import Image
import cv2

video = cv2.VideoCapture('input_video.mp4')
frame_count = 0

while True:
    ret, frame = video.read()
    if not ret:
        break
    img = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
    img.save(f"{frame_count:05d}.png")
    frame_count += 1

video.release()

Evaluation Metrics

Both short and long videos will be automatically evaluated in terms of 3 aspects.

Evaluation Aspects	Automatic Evaluation	Human Evaluation
`temporal_quality`	- VBench dimensions: `subject_consistency`, `background_consistency`, `motion_smoothness`, `dynamic_degree`,	Temporal consistency between frames, motion quality, motion strengths
`frame_wise_quality`	- VBench dimensions: `aesthetic_quality`, `imaging_quality`,	The quality of individual video frames
`text_alignment`	- VBench dimension: `overall_consistency`. and `CLIP Score`	Alignment between generated videos and text prompts

Additionally, long videos will be evaluated on subject_consistency as a stand-alone aspect via Human Evaluation.

Automatic Evaluation

1.Install Environment via Anaconda

conda create -n vbench-competition python=3.9
conda activate vbench-competition
pip install -r competitions/requirements.txt

# install PySceneDetect
pip install scenedetect[opencv] --upgrade
pip install ffmpeg

2.Evaluation Scripts

We support two forms of input, one is to directly input the path containing all mp4 format videos (using the --video_path flag). The other is to input the root directory of the videos saved in png format and the corresponding frame rate of the videos, and we will automatically convert the videos to the specified format and then evaluate them (using the --submission_path flag and --frame_rate flag).

Additionally, you need to use the --prompt_file flag to specify the category that the videos you want to evaluate belong to, whether it's short_video or long_video.

You can use the --dimension flag to specify one or more dimensions to be evaluated, and the --output_path flag to specify the path to store the results.

To perform evaluation, use the following script:

python run_eval.py --video_path <videos_path> --output_path <output_path>  --prompt_file <prompt_file> --dimension <dim1>

python run_eval.py --submission_path <submission_path> --frame_rate <fps> --output_path <output_path>  --prompt_file <prompt_file> --dimension <dim1> <dim2> <dim3>

For example:

python run_eval.py --submission_path ./Short_Videos --frame_rate 8 --output_path ./eval_results  --prompt_file ./short_prompt_list.txt --dimension temporal_quality frame_wise_quality text_alignment

The structure of submission_path should like this:

├── Short_Videos
│   ├── 0001_Close_up_of     # folder name corresponds to each prompt
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
│   ├── 0002_Turtle_swimming_in     
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...
│   ├── ...
│   ├── 0200_cruise_ship_in
│   │   ├── 00000.png
│   │   │── 00001.png
│   │   │── ...

3.Evaluation Results

The evaluation results will be saved in a JSON file in the following format, which includes the results of the target evaluation dimensions as well as the results of their corresponding sub-dimensions.

{
    "temporal_quality": [   # result of temporal_quality dimension
        0.8530498955750241,
        {
            "subject_consistency": [
                0.9986579449971517,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 14.991820216178894
                    },
                    ...
                ]
            ],
            "background_consistency": [
                0.9924527994791666,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 0.9943684895833333
                    },
                    ...
                ]
            ],
            "motion_smoothness": [
                0.9945638900362661,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 0.9937908449241242
                    },
                    ...
                ]
            ],
            "dynamic_degree": [
                0.0,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": false
                    },
                    ...
                ]
            ]
        }
    ],
    "frame_wise_quality": [   # result of frame_wise_quality dimension
        0.6555798406600952,
        {
            "aesthetic_quality": [
                0.5653051853179931,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 0.709746241569519
                    },
                    ...
                ]
            ],
            "imaging_quality": [
                0.7458544960021973,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 66.22727489471436
                    },
                    ...
                ]
            ]
        }
    ],
    "text_alignment": [   # result of text_alignment dimension
        0.2807383455336094,
        {
            "overall_consistency": [
                0.24468591958284377,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 0.2743408977985382
                    },
                    ...
                ]
            ],
            "clip_score": [
                0.316790771484375,
                [
                    ...
                    {
                        "video_path": "./evaluated_videos/0002_Turtle_swimming_in.mp4",
                        "video_results": 0.31317138671875
                    },
                    ...
                ]
            ]
        }
    ]
}

:black_nib: Citation

If you use VBench for evaluating your models, please consider citing our paper or repo:

 @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}
 }

 @article{huang2023vbenchgithub,
     author = {VBench Contributors},
     title = {VBench},
     year = {2023},
     publisher = {GitHub},
     journal = {GitHub repository},
     howpublished = {\url{https://github.com/Vchitect/VBench}},
 }