VBench/README.md

This repository contains the implementation of the following paper:

VBench: Comprehensive Benchmark Suite for Video Generative Models
Ziqi Huang^∗, Yinan He^∗, Jiashuo Yu^∗, Fan Zhang^∗, Chenyang Si, Yuming Jiang, Yuanhan Zhang, Tianxing Wu, Qingyang Jin, Nattapol Chanpaisit, Yaohui Wang, Xinyuan Chen, Limin Wang, Dahua Lin⁺, Yu Qiao⁺, Ziwei Liu⁺
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024

:fire: Updates

• [03/2024] :fire::fire: VBench-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 :fire::fire: We now support evaluating Image-to-Video (I2V) models. We also provide Image Suite.
• [03/2024] We support evaluating customized videos! See here for instructions.
• [01/2024] PyPI pacakge is released! . Simply pip install vbench.
• [12/2023] :fire::fire: VBench :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)

:mega: Overview

We propose VBench, a comprehensive benchmark suite for video generative models. We design a comprehensive and hierarchical Evaluation Dimension Suite 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 Prompt Suite as test cases, and sample Generated Videos from a set of video generation models. For each evaluation dimension, we specifically design an Evaluation Method Suite, which uses carefully crafted method or designated pipeline for automatic objective evaluation. We also conduct Human Preference Annotation for the generated videos for each dimension, and show that VBench evaluation results are well aligned with human perceptions. VBench can provide valuable insights from multiple perspectives.

:mortar_board: Evaluation Results

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)

:hammer: Installation

Install with pip

pip install vbench

To evaluate some video generation ability aspects, you need to install detectron2 via:

pip install detectron2@git+https://github.com/facebookresearch/detectron2.git

If there is an error during detectron2 installation, see here.

Download 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 installation, see here.

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. 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

    vbench evaluate --videos_path $VIDEO_PATH --dimension $DIMENSION

For example:

    vbench evaluate --videos_path "sampled_videos/lavie/human_action" --dimension "human_action"

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:

    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, and instructions for how to sample videos for evaluation.

: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:

 @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, Yinan He, Jiashuo Yu, Fan Zhang, Nattapol Chanpaisit, Xiaojie Xu.

Open-Sourced Repositories

This project wouldn't be possible without the following open-sourced repositories: AMT, UMT, RAM, CLIP, RAFT, GRiT, IQA-PyTorch, ViCLIP, and LAION Aesthetic Predictor.