ChronoMagic-Bench

Open-Sora Plan: Open-Source Large Video Generation Model Bin Lin, Yunyang Ge and Xinhua Cheng etc.

Helios: Real Real-Time Long Video Generation Model Shenghai Yuan, Jinfa Huang and Xianyi He etc.

OpenS2V-Nexus: A Detailed Benchmark and Million-Scale Dataset for Subject-to-Video Generation Shenghai Yuan, Xianyi He and Yufan Deng etc.

ConsisID: Identity-Preserving Text-to-Video Generation by Frequency Decomposition Shenghai Yuan, Jinfa Huang and Xianyi He etc.

MagicTime: Time-lapse Video Generation Models as Metamorphic Simulators Shenghai Yuan, Jinfa Huang and Yujun Shi etc.

📣 News

• ⏳⏳⏳ Evaluate more Text-to-Video Generation Models via ChronoMagic-Bench.
• [2026.03.08] 👋 We introduce Helios, a breakthrough video generation model that achieves minute-scale, high-quality video synthesis at 19.5 FPS on a single H100 GPU — without relying on conventional long video anti-drifting strategies or standard video acceleration techniques. Welcome to check Technical Report!
• [2024.12.31] 🔥 We further evaluate the widely popular Sora, which struggles to generate a diverse range of time-lapse videos effectively. This suggests that the generation of high-quality metamorphic videos remains an area in need of further exploration. The results are available here.
• [2024.09.30] 🔥 We have updated the calculation of the CHScore, making it more robust to temporally coherent disappearance of points. You can click here for detailed implementation.
• [2024.09.26] ✨ Our paper is accepted by NeurIPS 2024 D&B track as a spotlight present.
• [2024.08.13] 🔥 We further evaluate EasyAnimate-V3 and CogVideoX-2B. The results are available here.
• [2024.06.30] 🔥 We release the code of the "Multi-Aspect Data Preprocessing", which is used to process the ChronoMagic-Pro dataset. Please click here and here to see more details.
• [2024.06.29] 🔥 Support evaluating customized Text-to-Video models. The code and instructions are available in this repo.
• [2024.06.28] 🔥 We release the ChronoMagic-Pro and ChronoMagic-ProH datasets. The datasets include 460K and 150K time-lapse video-text pairs respectively and can be downloaded at HF-Dataset-Pro and HF-Dataset-ProH.
• [2024.06.27] 🔥 We release the arXiv paper and Leaderboard for ChronoMagic-Bench, and you can click here to read the paper and here to see the leaderboard.
• [2024.06.26] 🔥 We release the testing prompts, reference videos and generated results by different models in ChronoMagic-Bench, and you can click here to see more details.
• [2024.06.25] 🔥 All codes & datasets are coming soon! Stay tuned 👀!

😮 Highlights

ChronoMagic-Bench can reflect the physical prior capacity of Text-to-Video Generation Model.

Resources

• ChronoMagic-Bench: including 1649 time-lapse video-text pairs. (captioned by GPT-4o)
• ChronoMagic-Bench-150: including 150 time-lapse video-text pairs. (captioned by GPT-4o)
• ChronoMagic: including 2265 time-lapse video-text pairs. (captioned by GPT-4V)
• ChronoMagic-Pro: including 460K time-lapse video-text pairs. (captioned by ShareGPT4Video)
• ChronoMagic-ProH: including 150K time-lapse video-text pairs. (captioned by ShareGPT4Video)

:mega: Overview

In contrast to existing benchmarks, ChronoMagic-Bench emphasizes generating videos with high persistence and strong variation, i.e., metamorphic time-lapse videos with high physical prior content.

We specifically design four major categories for time-lapse videos (as shown below), including biological, human-created, meteorological, and physical videos, and extend these to 75 subcategories. Based on this, we construct ChronoMagic-Bench, comprising 1,649 prompts and their corresponding reference time-lapse videos.

:mortar_board: Evaluation Results

We visualize the evaluation results of various open-source and closed-source T2V generation models across ChronoMagic-Bench.

:trophy: Leaderboard

See numeric values at our Leaderboard :1st_place_medal::2nd_place_medal::3rd_place_medal:

or you can run it locally:

cd LeaderBoard
python app.py

⚙️ Requirements and Installation

We recommend the requirements as follows.

Environment

git clone --depth=1 https://github.com/PKU-YuanGroup/ChronoMagic-Bench.git
cd ChronoMagic-Bench
conda create -n chronomagic python=3.10
conda activate chronomagic

# install base packages
pip install -r requirements.txt

# install flash-attn
git clone https://github.com/Dao-AILab/flash-attention.git
cd flash-attention/csrc/layer_norm && pip install .
cd ../../../
rm -r flash-attention

Download Checkpoints

huggingface-cli download --repo-type model \
BestWishYsh/ChronoMagic-Bench \
--local-dir BestWishYsh/ChronoMagic-Bench

:bookmark_tabs: Benchmark Prompts

We provide evaluation prompt lists of ChronoMagic-Bench here or here. You can use this to sample videos for evaluation of your model. We also provide the reference videos for the corresponding evaluation prompts here.

:hammer: Usage

Use ChronoMagic-Bench to evaluate videos, and video generative models.

Prepare Videos for Evaluation

The generated videos should be named corresponding to the prompt ID in ChronoMagic-Bench and placed in the evaluation folder, which is structured as follows. We also provide input examples in the 'toy_video' .

# for open-source models
`-- input_video_folder
    `-- model_name_a
        |-- 1
        |   |-- 3d_printing_08.mp4
        |   `-- ...
        |-- 2
        |   |-- 3d_printing_08.mp4
        |   `-- ...
        `-- 3
            |-- 3d_printing_08.mp4
            `-- ...
    `-- model_name_b
        |-- 1
        |   |-- 3d_printing_08.mp4
        |   `-- ...
        |-- 2
        |   |-- 3d_printing_08.mp4
        |   `-- ...
        `-- 3
            |-- 3d_printing_08.mp4
            `-- ...
            
# for close-source models
-- input_video_folder
    |-- model_name_a
    |   |-- 3d_printing_08.mp4
    |   `-- animal_04.mp4
    |   `-- ...
    |-- model_name_b
    |   |-- 3d_printing_08.mp4
    |   `-- ...
    `-- ...

The filenames of all videos to be evaluated should be "videoid.mp4". For example, if the videoid is 3d_printing_08, the video filename should be "3d_printing_08.mp4". If this naming convention is not followed, the text relevance cannot be evaluated.

Get MTScore, CHScore and GPT4o-MTScore

We provide output examples in the 'results'. You can run the following commands for testing, then modify the relevant parameters (such as model_names, input_folder, model_pth and openai_api) to suit the text-to-video (T2V) generation model you want to evaluate.

python evaluate.py \
  --eval_type "open" \
  --model_names test \
  # or more than one model
  # --model_names name1 name2  \
  --input_folder toy_video \
  --output_folder results \
  --video_frames_folder video_frames_folder_temp \
  --model_pth_CHScore cotracker2.pth \
  --model_pth_MTScore InternVideo2-stage2_1b-224p-f4.pt \
  --num_workers 8 \
  --openai_api "sk-UybXXX" \

If you only want to evaluate any one of the metrics instead of calculating all of them, you can follow the step below. Before running, please modify the parameters in 'xxx.sh' as needed. (If you want to obtain the JSON to submit to the leaderboard, you can organize the output files in MTScore / CHScore / GPT4o-MTScore according to 'results' and then proceed with the following steps.)

# for MTScore
cd MTScore
bash get_chscore.sh

# for CHScore
cd CHScore
bash get_mtscore.sh

# for GPT4o-MTScore
cd GPT4o_MTScore
bash get_gp4omtscore.sh

Get UMT-FVD and UMTScore

Please refer to the folder UMT for how to compute the UMTScore.

Get File and Submit to Leaderboard

python get_uploaded_json.py \
  --input_path results/all \
  --output_path results

After completing the above steps, you will obtain ChronoMagic-Bench-Input.json, and then you need to manually fill the JSON with UMT-FVD and UMTScore (as we calculate them separately). Finally, you can submit the JSON to HuggingFace.

:surfer: Sampled Videos

To facilitate future research and to ensure full transparency, we release all the videos we sampled and used for ChronoMagic-Bench evaluation. You can download them on Hugging Face. We also provide detailed explanations of the sampled videos and detailed setting for the models under evaluation here.

🐳 ChronoMagicPro Dataset

ChronoMagic-Pro with 460K time-lapse videos, each accompanied by a detailed caption. We also released the 150K subset (ChronoMagic-ProH), which is a higher quality subset. All the dataset can be downloaded at here and here, or you can download it with the following command. Some samples can be found on our Project Page.

huggingface-cli download --repo-type dataset \
--resume-download BestWishYsh/ChronoMagic-Pro \  # or BestWishYsh/ChronoMagic-ProH
--local-dir BestWishYsh/ChronoMagic-Pro \  # or BestWishYsh/ChronoMagic-ProH
--local-dir-use-symlinks False

Please refer to the folder Multi-Aspect_Preprocessing for how ChronoMagic-Pro to process this data.

👍 Acknowledgement

• This project wouldn't be possible without the following open-sourced repositories: CoTracker, InternVideo2, UMT, FETV, VBench, Panda-70M, ShareGPT4Video and LAION Aesthetic Predictor.

🔒 License

• The majority of this project is released under the Apache 2.0 license as found in the LICENSE file.
• The service is a research preview. Please contact us if you find any potential violations. ([email protected])

✏️ Citation

If you find our paper and code useful in your research, please consider giving a star :star: and citation :pencil:.

@article{yuan2024chronomagic,
  title={Chronomagic-bench: A benchmark for metamorphic evaluation of text-to-time-lapse video generation},
  author={Yuan, Shenghai and Huang, Jinfa and Xu, Yongqi and Liu, Yaoyang and Zhang, Shaofeng and Shi, Yujun and Zhu, Rui-Jie and Cheng, Xinhua and Luo, Jiebo and Yuan, Li},
  journal={Advances in Neural Information Processing Systems},
  volume={37},
  pages={21236--21270},
  year={2024}
}

🤝 Contributors