| # [FiVE-Bench](https://arxiv.org/abs/2503.13684) (ICCV 2025) |
|
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| [FiVE-Bench: A Fine-Grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models](https://arxiv.org/abs/2503.13684) |
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| > [Minghan Li](https://scholar.google.com/citations?user=LhdBgMAAAAAJ&hl=en)<sup>1*</sup>, [Chenxi Xie](https://openreview.net/profile?id=%7EChenxi_Xie1)<sup>2*</sup>, [Yichen Wu](https://scholar.google.com/citations?hl=zh-CN&user=p53r6j0AAAAJ&hl=en)<sup>13</sup>, [Lei Zhang](https://scholar.google.com/citations?user=tAK5l1IAAAAJ&hl=en)<sup>2</sup>, [Mengyu Wang](https://scholar.google.com/citations?user=i9B02k4AAAAJ&hl=en)<sup>1†</sup><br> |
| > <sup>1</sup>Harvard University <sup>2</sup>The Hong Kong Polytechnic University <sup>3</sup>City University of Hong Kong<br> |
| > <sup>*</sup>Equal contribution <sup>†</sup>Corresponding Author |
| |
| 💜 [Leaderboard](https://huggingface.co/spaces/LIMinghan/FiVE-Bench-leaderboard) | |
| 💻 [GitHub](https://github.com/MinghanLi/FiVE-Bench) | |
| 🤗 [Hugging Face](https://huggingface.co/datasets/LIMinghan/FiVE-Fine-Grained-Video-Editing-Benchmark) |
| |
| 📝 [Project Page](https://sites.google.com/view/five-benchmark) | |
| 📰 [Paper](https://arxiv.org/abs/2503.13684) | |
| 🎥 [Video Demo](https://sites.google.com/view/five-benchmark) |
| |
| |
| <img src="assets/five_pipeline.png" alt="five-pipe" width="700"/> |
| |
| --- |
| ## Follow-up Works |
| - [DNAEdit (NeurIPS25 SpotLight)](https://github.com/xiechenxi99/DNAEdit_code) Direct Noise Alignment for Text-Guided Rectified Flow Editing |
| - [SplitFlow (NeurIPS25)](https://github.com/Harvard-AI-and-Robotics-Lab/SplitFlow) Flow Decomposition for Inversion-Free Text-to-Image Editing |
| - [DVRF (CVPR26)](https://arxiv.org/abs/2509.05342) Delta Velocity Rectified Flow for Text-to-Image Editing |
| |
| --- |
| ## 📝 TODO List |
| - [🔜] Add `Wan-Edit` demo page on HF |
| - [✅ Oct-30-2025] Add [leaderboard](https://huggingface.co/spaces/LIMinghan/FiVE-Bench-leaderboard) support 🔥🔥🔥🔥🔥 |
| - [✅ Oct-30-2025] Reorganized original results following Wan-Edit naming, kept only MP4s, [Google Drive](https://drive.google.com/file/d/1sNfds0tNrbCVZ5STdzlNiHdGUIe2e8KF/view?usp=sharing ). Thanks @Kunlin Yang. 🔥🔥🔥🔥🔥 |
| - [✅ Oct-28-2025] [The original results of all comparison methods](https://drive.google.com/drive/folders/1aTrLlUX9ug0vh6itBaDujwFvmlcgh_bE?usp=sharing) reported in the paper have been released for reference. 🔥🔥🔥🔥🔥 |
| - [✅ Aug-26-2025] Fix two issues: mp4_to_frames_ffmpeg and skip_timestep=17. Raw [quantitative results](results/8_wan_edit) of [`Wan-Edit'](models/wan-edit/) is included. |
| - [✅ Aug-05-2025] Release [`Wan-Edit'](models/wan-edit/) implementation |
| - [✅ Aug-05-2025] Release [`Pyramid-Edit`](models/pyramid-edit/) implementation |
| - [✅ Aug-02-2025] Add Wan-Edit results to HF for eval demo |
| - [✅ Aug-02-2025] Evaluation code released |
| - [✅ Mar-31-2025] Dataset uploaded to Hugging Face |
| |
| ## Human Evaluation Example via Netlify [Link1](https://five-all-models-0.netlify.app/) [Link2](https://five-all-models-1.netlify.app/) |
| |
| ## 🚀 Submit Your Results |
| |
| We welcome contributions! |
| If you’ve evaluated your method on FiVE-Bench, please share your results so we can include them in the [leaderboard](https://huggingface.co/spaces/LIMinghan/FiVE-Bench-leaderboard). |
| You can submit via a GitHub Issue or Pull Request following the leaderboard format. |
| |
| 📩 For large files or additional details, feel free to contact us directly. |
| |
| |
| ## 📚 Table of Contents |
| |
| - [FiVE-Bench Overview](#-five-bench-overview) |
| - [Running Your Model on FiVE-Bench](#running-your-model-on-five-bench) |
| - [Step 1: Download the Dataset and Set Up Evaluation Code](#️-step-1-download-the-dataset-and-set-up-evaluation-code) |
| - [Step 2: Apply Your Video Editing Method](#-step-2-apply-your-video-editing-method) |
| - [Step 3: Evaluate Editing Results](#-step-3-evaluate-editing-results) |
| - [Evaluate Editing Results](#-step-3-evaluate-editing-results) |
| - [Conventional Metrics](#-1-conventional-metrics-across-six-key-aspects) |
| - [FiVE-Acc: VLM-Based Metric](#-2-five-acc-a-vlm-based-metric-for-editing-success) |
| - [Citation](#-citation) |
| - [Acknowledgement](#️-acknowledgement) |
| |
| |
| --- |
| |
| ## 📦 FiVE-Bench Overview |
| |
| <img src="assets/five.png" alt="five" width="800"/> |
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| The FiVE-Bench dataset offers a rich, structured benchmark for fine-grained video editing. The dataset includes ***420*** high-quality source-target prompt pairs spanning ***six fine-grained video editing*** tasks: |
| 1. Object Replacement (Rigid) |
| 2. Object Replacement (Non-Rigid) |
| 3. Color Alteration |
| 4. Material Modification |
| 5. Object Addition |
| 6. Object Removal |
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| --- |
| ## Running Your Model on FiVE-Bench |
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| <img src="assets/five-acc.jpg" alt="five-bench1" width="800"/> |
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| --- |
| ### ⬇️ Step 1: Download the Dataset and Set Up Evaluation Code |
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| - Download the dataset from Hugging Face: 🔗 [FiVE-Bench on Hugging Face](https://huggingface.co/datasets/LIMinghan/FiVE-Fine-Grained-Video-Editing-Benchmark) |
|
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| - Follow the instructions in [Installation Guide](INSTALL.md) to download the dataset and install the evaluation code (`FiVE_Bench`). |
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| - Place the downloaded dataset in the directory: `./FiVE_Bench/data`. The data structure should looks like: |
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| ```json |
| 📁 /path/to/code/FiVE_Bench/data |
| ├── 📁 assets/ |
| ├── 📁 edit_prompt/ |
| │ ├── 📄 edit1_FiVE.json |
| │ ├── 📄 edit2_FiVE.json |
| │ ├── 📄 edit3_FiVE.json |
| │ ├── 📄 edit4_FiVE.json |
| │ ├── 📄 edit5_FiVE.json |
| │ └── 📄 edit6_FiVE.json |
| ├── 📄 README.md |
| ├── 📦 bmasks.zip |
| ├── 📁 bmasks |
| │ ├── 📁 0001_bus |
| │ ├── 🖼️ 00001.jpg |
| │ ├── 🖼️ 00002.jpg |
| │ ├── 🖼️ ... |
| │ ├── 📁 ... |
| ├── 📦 images.zip |
| ├── 📁 images |
| │ ├── 📁 0001_bus |
| │ ├── 🖼️ 00001.jpg |
| │ ├── 🖼️ 00002.jpg |
| │ ├── 🖼️ ... |
| │ ├── 📁 ... |
| ├── 📦 videos.zip |
| ├── 📁 videos |
| │ ├── 🎞️ 0001_bus.mp4 |
| │ ├── 🎞️ 0002_girl-dog.mp4 |
| │ ├── 🎞️ ... |
| ``` |
|
|
|
|
| --- |
| ### 🛠️ Step 2: Apply Your Video Editing Method |
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| Use your video editing method to edit the FiVE-Bench videos based on the provided text prompts and generate the corresponding edited results. |
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| <img src="assets/pyramid_edit_wan_edit.png" alt="rf-editing" width="700"/> |
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| Example implementations of our proposed rectified flow (RF)-based video editing methods are provided provided in the [`models/`](models/) directory: |
| |
| - **[Pyramid-Edit](models/README.md#pyramid-edit)**: Diffusion-based video editing using Pyramid-Flow architecture |
|
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| - **[Wan-Edit](models/README.md#wan-edit)**: Rectified flow-based video editing with Wan2.1-T2V-1.3B model |
| |
| |
| #### Quick Start with Provided Models |
| |
| **Run Pyramid-Edit:** |
| ```bash |
| # Setup model |
| cd models/pyramid-edit && mkdir -p hf/pyramid-flow-miniflux |
| # Download model checkpoint to hf/ directory |
| bash scripts/run_FiVE.sh |
| ``` |
| |
| **Run Wan-Edit:** |
| ```bash |
| # Setup model |
| cd models/wan-edit && mkdir -p hf/Wan2.1-T2V-1.3B |
| # Download model checkpoint to hf/ directory |
| bash scripts/run_FiVE.sh |
| ``` |
| |
| For detailed setup instructions and configuration options, see the [Models |
| Documentation](models/README.md). |
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|
| --- |
| ### 📊 Step 3: Evaluate Editing Results |
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| Follow the installation guide in [Installation Guide](INSTALL.md) to get the evaluation results. |
|
|
| ```bash |
| sh scripts/eval_FiVE.sh |
| ``` |
| *** |
| |
| **Evaluation Support Elements:** |
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| - **Editing Masks:** Generated using SAM2 to assist in localized metric evaluation. |
| |
| - **Editing Instructions:** Structured directives for each source-target pair to guide model behavior. |
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| FiVE-Bench provides **comprehensive evaluation** through **two major components**: |
|
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| #### 📐 1. Conventional Metrics (Across Six Key Aspects) |
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| These metrics quantitatively measure various dimensions of video editing quality: |
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| - **Structure Preservation** |
| - **Background Preservation** |
| (PSNR, LPIPS, MSE, SSIM outside the editing mask) |
| - **Edit Prompt–Image Consistency** |
| (CLIP similarity on full and masked images) |
| - **Image Quality Assessment** |
| ([NIQE](https://github.com/chaofengc/IQA-PyTorch)) |
| - **Temporal Consistency** |
| (MFS: [Motion Fidelity Score](https://github.com/diffusion-motion-transfer/diffusion-motion-transfer/blob/main/motion_fidelity_score.py)): |
| - **Runtime Efficiency** |
|
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| <img src="assets/five-bench-eval1.png" alt="five-bench-eval1" width="800"/> |
|
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| #### 🤖 2. FiVE-Acc: A VLM-based Metric for Editing Success |
| We use a vision-language model (VLM) to automatically assess whether the intended edits are reflected in the video outputs by asking it questions about the content. If the source video contains **a swan**, and the target prompt requests **a flamingo**. For the edited video, we ask |
| - **Yes/No Questions:** |
| - Is there **a swan** in the video? |
| - Is there **a flamingo** in the video? |
| |
| ✅ The edit is considered successful **only if** the answers are **"No"** to the first question and **"Yes"** to the second. |
| - **Multiple-choice Questions:** |
| - What is in the video? a) A swan b) A flamingo |
| |
| ✅ The edit is considered successful **if the model selects the correct target object** (e.g., **b) A flamingo**) and avoids selecting the original source object. |
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| FiVE-Acc evaluates editing success using a vision-language model (VLM) by asking content-related questions: |
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| - **YN-Acc**: Yes/No question accuracy |
| - **MC-Acc**: Multiple-choice question accuracy |
| - **U-Acc**: Union accuracy – success if any question is correct |
| - **∩-Acc**: Intersection accuracy – success only if all questions are correct |
| - **FiVE-Acc** ↑: Final score = average of all above metrics (higher is better) |
|
|
| <img src="assets/five-bench-eval2.png" alt="five-bench-eval2" width="400"/> |
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|
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| ### 📚 Citation |
|
|
| If you use **FiVE-Bench** in your research, please cite us: |
|
|
| ```bibtex |
| @article{li2025five, |
| title={Five: A fine-grained video editing benchmark for evaluating emerging diffusion and rectified flow models}, |
| author={Li, Minghan and Xie, Chenxi and Wu, Yichen and Zhang, Lei and Wang, Mengyu}, |
| journal={arXiv preprint arXiv:2503.13684}, |
| year={2025} |
| } |
| ``` |
|
|
| Recommended our recent papers on image/video editing: |
|
|
| ```bibtex |
| @article{xie2025dnaedit, |
| title={DNAEdit: Direct Noise Alignment for Text-Guided Rectified Flow Editing}, |
| author={Xie, Chenxi and Li, Minghan and Li, Shuai and Wu, Yuhui and Yi, Qiaosi and Zhang, Lei}, |
| journal={arXiv preprint arXiv:2506.01430}, |
| year={2025} # NeurIPS 2025 |
| } |
| ``` |
|
|
| ```bibtex |
| @article{beaudouin2025delta, |
| title={Delta Velocity Rectified Flow for Text-to-Image Editing}, |
| author={Beaudouin, Gaspard and Li, Minghan and Kim, Jaeyeon and Yoon, Sung-Hoon and Wang, Mengyu}, |
| journal={arXiv preprint arXiv:2509.05342}, |
| year={2025} |
| } |
| ``` |
|
|
| ### ❤️ Acknowledgement |
|
|
| Part of the code is adapted from [PIE-Bench](https://github.com/cure-lab/PnPInversion), [FlowEdit (ICCV25 Best Student Paper)](https://github.com/fallenshock/FlowEdit), [Pyramid-Flow](https://github.com/jy0205/Pyramid-Flow) and [Wan model](https://github.com/Wan-Video/Wan2.1). |
| We thank the authors for their excellent work and for making their code publicly available. |
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|
