Implementation of FOMM with AWP and MRAA with AWP in Facial Prior Guided Micro-Expression Generation (IEEE TIP 2024).
CASE | Driving | FOMM | FOMM w/ EWP | FOMM w/ AWP | MRAA | MRAA w/ AWP |
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- To facilitate micro-expression observations and reduce the storage space of this repository, all Gifs have been slowed down and compressed.
- Case 1-3: Positive. Case 4: Negative. Case 5: Surprise.
-
Procedures of running FOMM_with_AWP and MRAA_with_AWP are the same.
-
Prepare your dataset. CASME2, SAMM, and SMIC-HS are recommended. See related preparation instructions in this repository.
-
Divide into
your_dataset/train
andyour_dataset/test
-
Create or modify
yaml
format fileyour_dataset_train.yaml
in./config
-
-
Train
python run.py --config config/your_dataset_train.yaml
Log, parameters and checkpoints would be saved in
./log
-
Test
Create or modify
csv
format fileyour_dataset_test.csv
in./data
python run.py --config config/my_dataset_test.yaml --mode animate --checkpoint path/to/checkpoint
Generated videos would be saved in
path/to/checkpoint/animation
If you find this work helpful in your research, please consider citing:
@ARTICLE{zhang2024tipfacial,
author={Zhang, Yi and Xu, Xinhua and Zhao, Youjun and Wen, Yuhang and Tang, Zixuan and Liu, Mengyuan},
journal={IEEE Transactions on Image Processing},
title={Facial Prior Guided Micro-Expression Generation},
year={2024},
volume={33},
number={},
pages={525-540},
doi={10.1109/TIP.2023.3345177}
}
@inproceedings{zhang2021acmmmfacial,
author = {Zhang, Yi and Zhao, Youjun and Wen, Yuhang and Tang, Zixuan and Xu, Xinhua and Liu, Mengyuan},
title = {Facial Prior Based First Order Motion Model for Micro-expression Generation},
year = {2021},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {4755–4759},
numpages = {5},
series = {MM '21},
doi = {10.1145/3474085.3479211}
}