VideoX-Fun/VBench/vbench2_beta_long/README.md

VBench-Long (part of VBench++)

VBench++ now supports evaluating long video generative models.

1. Video Splitting

We split the long video into video clips in two steps

:hammer: Setup Repository and Environment

git clone https://github.com/Vchitect/VBench.git

# create conda environment, following instructions in VBench README
pip install -r VBench/requirements.txt
pip install VBench

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

1.1 Bypass Scene Cuts

First, we use PySceneDetect to split a long video into multiple semantically consistent short clips and save these clips. After this step, each split video clip ideally contains no scene cuts.

For example

from vbench2_beta_long.utils import split_video_into_scenes
split_video_into_scenes(video_path, output_dir, threshold)

1.2 Create Slow-Fast Branches

Next, we split the videos from the previous step into shorter fixed-length clips to enable the slow-fast evaluation introduced in the next section. Since some evaluation dimensions use models trained on longer video clips, such as UMT and ViCLIP, for human_action and overall_consistency, we established different fixed-length durations for different dimensions. These durations can be found in vbench2_beta_long/configs/clip_length_mix.yaml.

Usage:

from vbench2_beta_long.utils import split_video_into_clips
split_video_into_clips(video_path, base_output_dir, duration, fps)

Note: The two video splitting steps have been integrated into VBench-Long for automatic execution, so users do not need to manually perform this processing in advance.

2. Slow-Fast Approach to Evaluate Temporal Consistency

Previously, VBench evaluated temporal consistency primarily by calculating the consistency between adjacent video frames. However, for longer videos, it is also crucial to consider the long-range consistency of background scenes and foreground subjects. To address this, we have adopted a slow-fast approach for evaluating temporal consistency.

• Slow Branch: This high-frame-rate branch includes every frame in the short video clip. The slow branch evaluation follows VBench's short video evaluation approach.
• Fast branch: This low-frame-rate branch extracts the first frame of each very short video clip from the same long video. We then evaluate the long-range consistency using a new set of feature extractors that emphasize high-level visual similarity over lower-level details.

3. Usage

3.1 Evaluation on the Standard Prompt Suite of VBench

You can use the command below to evaluate long videos sampled based on the standard prompt of VBench:

python vbench2_beta_long/eval_long.py \
    --videos_path $videos_path \
    --dimension $dimension \ 
    --mode 'long_vbench_standard' \
    --dev_flag \

For dimension temporal_flickering, static filter should be implemented before evaluaing videos. We ensembled static filter function into preprocess for VBench-Long, and you can use flag static_filter_flag to execute static filter, such as:

python vbench2_beta_long/eval_long.py \
    --videos_path $videos_path \
    --dimension 'temporal_flickering' \ 
    --mode 'long_vbench_standard' \
    --dev_flag \
    --static_filter_flag

3.2 Evaluation on Your Own Videos

For long video evaluation, we support customized videos / prompts for the following dimensions: subject_consistency, background_consistency, motion_smoothness, dynamic_degree, aesthetic_quality, imaging_quality

python vbench2_beta_long/eval_long.py \
    --videos_path $videos_path \
    --dimension $dimension \ 
    --mode 'long_custom_input' \
    --dev_flag

3.3 Automatic Evaluation Script

We provide the evaluate_long.sh script for automating the evaluation across all dimensions. To use the script, simply provide the path to your videos in the following command and run it:

sh vbench2_beta_long/evaluate_long.sh $VIDEOS_PATH

3.4 Example of Evaluating OpenSoraPlan

We have provided scripts to download OpenSoraPlanv1.1 samples, and the corresponding evaluation scripts.

# download sampled videos of OpenSoraPlan
sh scripts/download_OpenSoraPlan.sh

# evaluate OpenSoraPlan
sh scripts/evaluate_OpenSoraPlan.sh

:black_nib: Citation

If you find VBench-Long (a component of VBench++) useful in your work, please consider citing the following papers:

 @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{huang2025vbench++,
     title={{VBench++}: Comprehensive and Versatile Benchmark Suite for Video Generative Models},
     author={Huang, Ziqi and Zhang, Fan and Xu, Xiaojie and He, Yinan and Yu, Jiashuo and Dong, Ziyue and Ma, Qianli and Chanpaisit, Nattapol and Si, Chenyang and Jiang, Yuming and Wang, Yaohui and Chen, Xinyuan and Chen, Ying-Cong and Wang, Limin and Lin, Dahua and Qiao, Yu and Liu, Ziwei},
     journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
     year={2025},
     doi={10.1109/TPAMI.2025.3633890}
 }

:hearts: Acknowledgement

VBench-Long is currently maintained by Ziqi Huang and Qianli Ma.

In addition to the open-sourced repositories used in VBench, we also made use of PySceneDetect, DINOv2, DreamSim.