Micro Expression Generation with Thin-plate Spline Motion Model and Face Parsing

Installation

We support python3.(Recommended version is Python 3.9). To install the dependencies run:

pip install -r requirements.txt

YAML configs

In our method, all the configurations are contained in the file config/Mixed_data-10-8-wMaskWarp-aug.yaml.

Datasets

1. Download three datasets CASME II, SMIC, SAMM

2. Download the test dataset megc2022-synthesis

3. Download the shape_predictor_68_face_landmarks.dat and put it in the datasetfolder

4. Put the three training set and one test set in the datasetfolder. The file tree is shown as follows:

.
├── CASMEII
│   ├── CASME2-coding-20190701.xlsx
│   ├── CASME2_RAW_selected
├── copy_.py
├── crop.py
├── megc2022-synthesis
│   ├── source_samples
│   ├── target_template_face
├── SAMM
│   ├── SAMM
│   ├── SAMM_Micro_FACS_Codes_v2.xlsx
├── shape_predictor_68_face_landmarks.dat
└── SMIC
    ├── SMIC_all_raw

5. Run the following code

cd dataset
python crop.py
python copy_.py
mv Mixed_dataset_test.csv ./Mixed_dataset
cd ..

the root of the preprocessed dataset is ./dataset/Mixed_dataset

6. Download the train_mask.tar.gz and unzip it, then put it in the ./dataset/Mixed_dataset/train_mask

Training

To train a model on specific dataset run:

CUDA_VISIBLE_DEVICES=0,1,2,3 python run.py \
        --config config/Mixed_data-10-8-wMaskWarp-aug.yaml \
        --device_ids 0,1,2,3

A log folder named after the timestamp will be created. Checkpoints, loss values, reconstruction results will be saved to this folder.

Micro expression generation

CUDA_VISIBLE_DEVICES=0 python demo.py \
    --config config/Mixed_data-10-8-wMaskWarp-aug.yaml  \
    --checkpoint 'path to the checkpoint' \
    --result_video './ckpt/relative' \
    --mode 'relative'

Our provided model can be downloaded here The final results are in the folder ./ckpt/relative .

Acknowledgments

The main code is based upon FOMM, MRAA and TPS

Thanks for the excellent works!