sooyek/EditVerseBench

EditVerse

This repository contains the instruction-based video editing evaluation benchmark for EditVerseBench in paper "EditVerse: A Unified Framework for Editing and Generation via In-Context Learning".

Xuan Ju¹², Tianyu Wang¹, Yuqian Zhou¹, He Zhang¹, Qing Liu¹, Nanxuan Zhao¹, Zhifei Zhang¹, Yijun Li¹, Yuanhao Cai³, Shaoteng Liu¹, Daniil Pakhomov¹, Zhe Lin¹, Soo Ye Kim^1*, Qiang Xu^2*
1Adobe Research ²The Chinese University of Hong Kong ³Johns Hopkins University ^*Corresponding Author

🌐 Project Page || 📜 Arxiv || ✨ Slides || 👀 Comparison || 💻 Evaluation Code

⏬ Download Benchmark

(1) Clone the EditVerseBench Repository

git lfs install
git clone https://huggingface.co/datasets/EditVerse/EditVerseBench

(2) Download the Videos

The original source videos cannot be directly distributed due to licensing restrictions. Instead, you can download them using the provided script with the Pixabay API. (The network connection may occasionally fail, so you might need to run the script multiple times.)

⚠️ Note: Please remember to revise the API key to your own key in download_source_video.py. You can find the API key here (marked in Parameters-key(required) on the website). The API is free but you need to sign up an account to have the API key.

python download_source_video.py

(3) Unpack comparison results (Optional)

Extract the comparison results and remove the archive:

cd EditVerseBench
tar -zxvf EditVerse_Comparison_Results.tar.gz
rm EditVerse_Comparison_Results.tar.gz

✨ Benchmark Results

Method	VLM evaluation	Video Quality	Text Alignment		Temporal Consistency
	Editing Quality ↑	Pick Score ↑	Frame ↑	Video ↑	CLIP ↑	DINO ↑
Attention Manipulation (Training-free)
TokenFlow	5.26	19.73	25.57	22.70	98.36	98.09
STDF	4.41	19.45	25.24	22.26	96.04	95.22
First-Frame Propagation (w/ End-to-End Training)
Señorita-2M	6.97	19.71	26.34	23.24	98.05	97.99
Instruction-Guided (w/ End-to-End Training)
InsV2V	5.21	19.39	24.99	22.54	97.15	96.57
Lucy Edit	5.89	19.67	26.00	23.11	98.49	98.38
Ours (Ours)	7.65	20.07	26.73	23.93	98.56	98.42
Closed-Source Commercial Models
Runway Aleph	7.44	20.42	27.70	24.27	98.94	98.60

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

@article{ju2025editverse,
  title   = {EditVerse: Unifying Image and Video Editing and Generation with In-Context Learning},
  author  = {Xuan Ju and Tianyu Wang and Yuqian Zhou and He Zhang and Qing Liu and Nanxuan Zhao and Zhifei Zhang and Yijun Li and Yuanhao Cai and Shaoteng Liu and Daniil Pakhomov and Zhe Lin and Soo Ye Kim and Qiang Xu},
  journal = {arXiv preprint arXiv:2509.20360},
  year    = {2025},
  url     = {https://arxiv.org/abs/2509.20360}
}