FiVE-Bench (ICCV 2025)

FiVE-Bench: A Fine-Grained Video Editing Benchmark for Evaluating Emerging Diffusion and Rectified Flow Models

Minghan Li^1*, Chenxi Xie^2*, Yichen Wu¹³, Lei Zhang², Mengyu Wang^1†
1Harvard University ²The Hong Kong Polytechnic University ³City University of Hong Kong
^*Equal contribution ^†Corresponding Author

💜 Leaderboard   |   💻 GitHub   |   🤗 Hugging Face  

📝 Project Page   |   📰 Paper   |   🎥 Video Demo  

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Follow-up Works

• DNAEdit (NeurIPS25 SpotLight) Direct Noise Alignment for Text-Guided Rectified Flow Editing
• SplitFlow (NeurIPS25) Flow Decomposition for Inversion-Free Text-to-Image Editing
• DVRF (CVPR26) Delta Velocity Rectified Flow for Text-to-Image Editing

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📝 TODO List

• [🔜] Add Wan-Edit demo page on HF
• [✅ Oct-30-2025] Add leaderboard support 🔥🔥🔥🔥🔥
• [✅ Oct-30-2025] Reorganized original results following Wan-Edit naming, kept only MP4s, Google Drive. Thanks @Kunlin Yang. 🔥🔥🔥🔥🔥
• [✅ Oct-28-2025] The original results of all comparison methods 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 of `Wan-Edit' is included.
• [✅ Aug-05-2025] Release `Wan-Edit' implementation
• [✅ Aug-05-2025] Release 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 Link2

🚀 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. 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
• Running Your Model on FiVE-Bench
  • Step 1: Download the Dataset and Set Up Evaluation Code
  • Step 2: Apply Your Video Editing Method
  • Step 3: Evaluate Editing Results
• Evaluate Editing Results
  • Conventional Metrics
  • FiVE-Acc: VLM-Based Metric
• Citation
• Acknowledgement

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📦 FiVE-Bench Overview

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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⬇️ Step 1: Download the Dataset and Set Up Evaluation Code

• Download the dataset from Hugging Face: 🔗 FiVE-Bench on Hugging Face

• Follow the instructions in Installation Guide to download the dataset and install the evaluation code (FiVE_Bench).

• Place the downloaded dataset in the directory: ./FiVE_Bench/data. The data structure should looks like:

  📁 /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
  │   ├── 🎞️ ...

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🛠️ Step 2: Apply Your Video Editing Method

Use your video editing method to edit the FiVE-Bench videos based on the provided text prompts and generate the corresponding edited results.

Example implementations of our proposed rectified flow (RF)-based video editing methods are provided provided in the models/ directory:

- **[Pyramid-Edit](models/README.md#pyramid-edit)**: Diffusion-based video editing using Pyramid-Flow architecture

- **[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:

# 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:

# 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.

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📊 Step 3: Evaluate Editing Results

Follow the installation guide in Installation Guide to get the evaluation results.

sh scripts/eval_FiVE.sh

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Evaluation Support Elements:

• Editing Masks: Generated using SAM2 to assist in localized metric evaluation.

• Editing Instructions: Structured directives for each source-target pair to guide model behavior.

FiVE-Bench provides comprehensive evaluation through two major components:

📐 1. Conventional Metrics (Across Six Key Aspects)

These metrics quantitatively measure various dimensions of video editing quality:

• 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)
• Temporal Consistency
  (MFS: Motion Fidelity Score):
• Runtime Efficiency

🤖 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.

FiVE-Acc evaluates editing success using a vision-language model (VLM) by asking content-related questions:

• 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)

📚 Citation

If you use FiVE-Bench in your research, please cite us:

@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:

@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
}

@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, FlowEdit (ICCV25 Best Student Paper), Pyramid-Flow and Wan model.
We thank the authors for their excellent work and for making their code publicly available.