Installation Guide
Table of Contents
- Step 1: Create Conda Environment
- Step 2: Install FiVE-Bench and Dependencies
- Step 3: Run FiVE-Bench Evaluation
Step 1: Create Conda Environment
conda create -n five-bench python=3.11 -y
conda activate five-bench
conda install pytorch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 pytorch-cuda=12.1 -c pytorch -c nvidia
Step 2: Install FiVE-Bench and Dependencies
⭐ After installation, your directory structure should look like this:
📁 /path/to/code
├── 📁 co-tracker
├── 📁 FiVE-Bench
├── 📁 IQA-PyTorch
Make sure all dependencies for each subproject are installed accordingly.
⚠️ NOTE: Replace
/path/to/codein the./config.yamlfile with the actual path to your code directory.
⬇️ Install Co-Tracker and IQA Repos
Motion Fidelity Score (MFS) @ Co-Tracker: To evaluate temporal consistency using MFS, install Co-Tracker in the following path:
./code/co-tracker.cd ./code git clone https://github.com/facebookresearch/co-tracker cd co-tracker pip install -e . pip install matplotlib flow_vis tqdm tensorboard mkdir -p checkpoints cd checkpoints # download the offline (single window) model wget https://huggingface.co/facebook/cotracker3/resolve/main/scaled_offline.pth cd ..Image Quality Assessment (IQA) @ NIQE: To evaluate image quality with NIQE, install IQA-PyTorch under
./code/IQA-PyTorch. Then, replace the defaultinference_iqa.pywith the version provided in our repo at./files/inference_iqa.py.# Install with pip pip install pyiqa # Install latest github version pip uninstall pyiqa # if have older version installed already pip install git+https://github.com/chaofengc/IQA-PyTorch.git # Install with git clone cd ./code git clone https://github.com/chaofengc/IQA-PyTorch.git cd IQA-PyTorch # pip install -r requirements.txt python setup.py develop💡 Don’t forget to replace
inference_iqa.py:cp ../../files/inference_iqa.py ./inference_iqa.py
⬇️ Clone FiVE-Bench Repository
Download dataset and install the evaluation code
cd ./code
# evaluation code
git clone https://github.com/minghanli/FiVE-Bench.git
pip install -r requirements.txt
# FiVE-Bench dataset
cd ./FiVE-Bench
git clone https://huggingface.co/datasets/LIMinghan/FiVE-Fine-Grained-Video-Editing-Benchmark
mv FiVE-Fine-Grained-Video-Editing-Benchmark data
unzip bmasks.zip images.zip videos.zip
The data structure should looks like:
📁 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 3: Run FiVE-Bench Evaluation
🎯 Evaluation Example: Wan-Edit
As an example, you can run evaluation using the Wan-Edit results. We use the edited results in ./data/results/Wan-Edit with prompts from ./data/edit_prompt/edit5_FiVE.json. Then run:
cd FiVE-Bench
sh scripts/eval_FiVE.sh --annotation_mapping_files "data/edit_prompt/edit5_FiVE.json" --tgt_methods "8_Wan_Edit"
The evaluation result files should be found in:
📁 outputs
├── 📄 edit5_FiVE_evaluation_result_frame_stride8.csv
├── 📄 edit5_FiVE_evaluation_result_frame_stride8_avg.csv
🎯 Evaluate Your Own Method
If you want to evaluate your own method, you can modify the following parameters in config.yaml and evaluation/evaluate.py:
root_tgt_video_folder: the root directory where your edited videos are storedall_tgt_video_folders: a list of subfolders corresponding to your method(s)
Updating these paths allows the evaluation script to locate and assess your results accordingly.