Facial Prior Based First Order Motion Model for Micro-expression Generation

This repository contains the source code for the paper Facial Prior Based First Order Motion Model for Micro-expression Generation included in MM '21: Proceedings of the 29th ACM International Conference on Multimedia.

FME Challenge 2021 and ACM Multimedia 2021

Official Site: https://megc2021.github.io/index.html

0. Table of Contents

• 1. Authors & Maintainers

• 2. Change Log

• 3. Results in GIF

• 4. Run the Code

• 5. Additional Resources

  • 5.1 Training Datasets
  • 5.2 Source Videos & Target Faces for Evaluation in MEGC2021
  • 5.3 Pre-trained Checkpoint

• 6. License

• 7. Citation

1. Authors & Maintainers

• Yi Zhang|@zylye123
• Youjun Zhao|@zhaoyjoy
• Yuhang Wen|@Necolizer
• Zixuan Tang|@sysu19351118
• Xinhua Xu|@sysu19351158

2. Change Log

• [2021/12/16] Readme & Filename & Preprocess Utils Updated. Additional resources have been provided in Section 5.
• [2021/10/20] Our paper has been published online at https://doi.org/10.1145/3474085.3479211.
• [2021/10/13] Code Updated.
• [2021/07/12] Qualitative results are presented in GIF format.
• [2021/07/10] Create repository, add basic information, and upload code.

3. Results in GIF

No.	Source	FOMM	MRAA	OurSA	OurSM	OurCA	OurMX
1
2
3

NOTICE: For visual presentation, all the GIFs have been slowed down.

Source == Source videos, also called driving videos

FOMM == First Order Motion Model for Image Animation

MRAA == Motion Representations for Articulated Animation

OurSA == Our method, training on SAMM

OurSM == Our method, training on SMIC-HS

OurCA == Our method, training on CASMEⅡ

OurMIX == Our method, training on SAMM + SMIC-HS + CASMEⅡ

4. Run the Code

1. Installation. We support python3. To install the dependencies run:

   pip install -r requirements.txt
   

   P.S. The Original FOMM requirments.txt says that torch==1.0.0 , but it seems that torch whose version is higher than 1.0.0 also works, provided that all the dependency conflicts are solved.

2. Prepare your own dataset. Divide it into ./data/your_dataset/train and ./data/your_dataset/test.

   • For our task, CASMEⅡ, SAMM and SMIC-HS are used for training. See Section 5.1.
   • In MEGC 2021 FME Generation Task, particular Source Videos & Target Faces are provided for test/evaluation. See Section 5.2.

3. Create new yaml format files, or modify your_dataset_train.yaml & your_dataset_test.yaml in ./config. Set your expected evaluation pair configuration by modify ./data/your_dataset_test.csv, which indicates which videos serve as source videos and which images serve as target faces.

   • If you would like to take the same Training & Testing Data as ours, there's no need for modifications to these files.

4. Preprocess your dataset.

   The input frame shape is (256, 256, 3) (though you could change it by altering your_dataset_train.yaml & your_dataset_test.yaml in ./config). We provide preprocess utils in ./preprocess.py that might be helpful when preprocessing your dataset. The following steps illustrate how we did it for our training & testing data.

   1. Use dlib.get_frontal_face_detector() to detect the face in the onset frame and get its bounding box. This would treat as the face bounding box of the whole micro-expression video, as in FME videos, head movements are really subtle. The bounding box of the onset frame is usually large enough.
   2. Crop sequence of video frames using the 1st bounding box and resize them to $256\times 256$.
   3. (optional) For each video frame, convert RGB to GARY, and then in channel dimension merge itself 3 times to get a 3-channel grayscale. The reason why we did it is that, video frames in SAMM dataset are in this form while the other two datasets are not. To train on SAMM + SMIC-HS + CASMEⅡ and ensure the consistency of input data, we did it for SMIC-HS and CASMEⅡ, though the original data are colored images. It is worth noting that training on grayscale version of dataset would results in grayscale generated videos. This step is optional if you want colored generation results.
   4. Store frame sequence of each FME video in each individual folder.

5. If you take the same Training & Testing Data as ours, your final directory tree should look like this:

   Facial-Prior-Based-FOMM
   ├── data
   |   ├── your_dataset_test.csv
   |   └── your_dataset
   |    	├── train
   |       |   ├── 006_1_2
   |       |   |	├── 006_05562.png
   |       |   |	├── 006_05563.png
   |       |   |   ├── ...
   |       |   ├── 006_1_3
   |       |   ├── ... 
   |       └── test
   |           ├── normalized_westernMale
   |           |   └── normalized_westernMale_gray.png
   |           ├── normalized_asianFemale
   |           ├── Surprise_007_7_1
   |           |   ├── 007_7030.png
   |           |   ├── ...
   |           ├── Positive_022_3_3
   |           ├── Negative_018_3_1
   |           ├── Surprise_EP01_13
   |           ├── Positive_EP01_01f
   |           ├── Negative_EP19_06f
   |           ├── Surprise_s20_sur_01
   |           ├── Positive_s3_po_05
   |           └── Negative_s11_ne_02
   ├── config
   |   ├── your_dataset_train.yaml
   |   └── your_dataset_test.yaml
   ├── ...
   

   In ./data/your_dataset/test, subfolders normalized_westernMale & normalized_asianFemale serve as target faces. Each of them only contains one face image. The other subfolders serve as source videos, holding sequences of video frames.

6. Run kpmaker.py.

   python kpmaker.py
   

   Key points of each video in your dataset would be generated in./keypoint_folder.

7. Train

   python run.py --config config/your_dataset_train.yaml
   

   Log and checkpoints would be saved in ./log.

8. Test/Evaluation

   python run.py --config config/your_dataset_test.yaml --mode animate --checkpoint path-to-checkpoint
   

   Generated videos would be saved in path-to-checkpoint/animation.

NOTICE: Github repository of FOMM provides details about how to run the code using multi-GPU and many other detailed tricks. If you encounter problems when running this code, please refer to that repository. And if your problem still exists, please let us know by creating a new issue.

5. Additional Resources

5.1 Training Datasets

Datasets	Website Link	Online Paper
CASMEⅡ	http://fu.psych.ac.cn/CASME/casme2.php	https://pubmed.ncbi.nlm.nih.gov/24475068/
SAMM	http://www2.docm.mmu.ac.uk/STAFF/M.Yap/dataset.php	https://ieeexplore.ieee.org/document/7492264
SMIC (including SMIC-HS)	https://www.oulu.fi/cmvs/node/41319	https://ieeexplore.ieee.org/document/6553717

5.2 Source Videos & Target Faces for Evaluation in MEGC2021

Source videos and target faces for evaluation (pls put them in your_dataset/test) can be found at https://megc2021.github.io/images/MEGC2021_generation.zip , which is also provided at https://megc2021.github.io/agenda.html , in section Description of Challenge tasks.

5.3 Pre-trained Checkpoint

Checkpoint that achieves the final results in our paper can be found under following link: Google Drive.

It was trained on the composite dataset of CASME2, SAMM and SMIC-HS.

6. License

MIT

7. Citation

If you find this work or code helpful in your research, please consider citing:

@inproceedings{10.1145/3474085.3479211,
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},
isbn = {9781450386517},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3474085.3479211},
doi = {10.1145/3474085.3479211},
abstract = {Spotting facial micro-expression from videos finds various potential applications
in fields including clinical diagnosis and interrogation, meanwhile this task is still
difficult due to the limited scale of training data. To solve this problem, this paper
tries to formulate a new task called micro-expression generation and then presents
a strong baseline which combines the first order motion model with facial prior knowledge.
Given a target face, we intend to drive the face to generate micro-expression videos
according to the motion patterns of source videos. Specifically, our new model involves
three modules. First, we extract facial prior features from a region focusing module.
Second, we estimate facial motion using key points and local affine transformations
with a motion prediction module. Third, expression generation module is used to drive
the target face to generate videos. We train our model on public CASME II, SAMM and
SMIC datasets and then use the model to generate new micro-expression videos for evaluation.
Our model achieves the first place in the Facial Micro-Expression Challenge 2021 (MEGC2021),
where our superior performance is verified by three experts with Facial Action Coding
System certification. Source code is provided in https://github.com/Necolizer/Facial-Prior-Based-FOMM.},
booktitle = {Proceedings of the 29th ACM International Conference on Multimedia},
pages = {4755–4759},
numpages = {5},
keywords = {facial micro-expression, facial landmark, deep learning, micro-expression generation, generative adversarial network},
location = {Virtual Event, China},
series = {MM '21}
}