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EmotionRecognition

README.md

Emotion Recognition

This project accompanies the EMOCA release. Here we provide training and testing code for:

  • image-based emotion recognition networks
    • EmoNet
    • vanilla vision backbones (ResNet, VGG, SWIN)
  • EMOCA-based emotion recognotion

The provided models were trained and tested on AffectNet.

emotion_recog

Installation

Follow the steps at the root of this repo. If for some reason the environment from there is not valid, create one using a .yml file from envs. After that you should be able to run the demos.

Pre-trained models

All the provided models have been trained on AffectNet, either from scratch (the image-based backbones) or finetuned based on fixed pretrained face reconstruction nets.

Use download_assets.sh to download and unzip them:

cd demos 
bash download_assets.sh

Then activate your environment:

conda activate work36_cu11

Demos

Single-image based emotion recognition

If you want to run Emotion Recognition on images, run the following

python demos/test_emotion_recognition_on_images.py --input_folder <path_to_images> --output_folder <set_your_output_path>  --modeltype (image|3dmm) --model_name (ResNet50|SWIN-B|EMOCA-emorec|EMOCA_detail-emorec)

The script will detect faces in every image in the folder output the results that you specify with --save_images, --save_codes, --save_mesh to the output folder.

For example, for inference with EMOCA run:

python demos/test_emotion_recognition_on_images.py --input_folder <path_to_images> --output_folder <set_your_output_path>  --modeltype 3dmm --model_name EMOCA-emorec

and for inference with Resnet50 run:

python demos/test_emotion_recognition_on_images.py --input_folder <path_to_images> --output_folder <set_your_output_path>  --modeltype image --model_name ResNet50

For example, running the following will run emotion recognition with EMOCA on a number of test images:

python demos/test_emotion_recognition_on_images.py --modeltype 3dmm --model_name EMOCA-emorec

See demos/test_emotion_recognition_on_images.py for further details.

Training

  1. Process AffectNet data as described in EMOCA
  2. Pick one of the configuration files based on what network you want to train (resnet50.yaml, emoca_coarse.yaml, emoca_detail.yaml, swin.yaml). For EMOCA-based models, you will need to change paths to

Citation

If you use this work in your publication, please cite the following publications:

@inproceedings{EMOCA:CVPR:2022,
  title = {{EMOCA}: {E}motion Driven Monocular Face Capture and Animation},
  author = {Danecek, Radek and Black, Michael J. and Bolkart, Timo},
  booktitle = {Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages = {},
  year = {2022}
}

Contributing

Pull requests are welcome! Please report crashes and/or bugs.