Daksitha / lets_face_it

This is the official implementation for IVA'20 Best Paper Award paper "Let's Face It: Probabilistic Multi-modal Interlocutor-aware Generation of Facial Gestures in Dyadic Settings"

Let's face it

This repository contains a PyTorch based implementation of the framework for the paper "Let's face it: Probabilistic multi-modal interlocutor-aware generation of facial gestures in dyadic settings.", which received the Best Paper Award at IVA'20.

Please read more on the project website

Installations

1. Install Docker We recommend installing the latest version of the Docker as described here

2. Setup GPU usage for the docker By following the instructions in the official tutorial

 

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Model training and testing

1. Build the Docker image

docker build -f containers/glow_Dockerfile . -t lets_face_it_glow

2. Train the model

docker run --gpus 1 -v <path/to/the/dataset>:/data lets_face_it_glow python code/glow_pytorch/train.py code/glow_pytorch/hparams/final_model.yaml

where path/to/the/dataset should be replaced with the path to the dataset on your machine

Customizing Most of the model parameters are defined in code/glow_pytorch/hparams/final_model.yaml. Other configurations are set in code/config.toml

Visualization

1. Build the docker

docker build -f containers/visualize_Dockerfile . -t lets_face_it_visualize

2. Get the models

• Download FLAME 2019 model from here. You need to sign up and agree to the model license for access to the model. Copy the downloaded model inside the models/flame_model folder.
• Download Landmark embedings from RingNet Project. Copy it inside the models/flame_model folder.

3. Run the render server

docker run -v $(pwd)/models:/workspace/models -it -p 8000:8000 lets_face_it_visualize

4. Try the example code There is some example code for rendering in code/examples/visualize_example.py. This example assumes that you have downloade the facial feature dataset. After rendring you will get back a json response from the server with a URL which can be used to access the video.

Feature extraction

python code/fextract_features.py

Citation

If you use this code in your research please cite the paper:

@inproceedings{jonell2020letsfaceit,
  title={Let's face it: Probabilistic multi-modal interlocutor-aware generation of facial gestures in dyadic settings},
  author={Jonell, Patrik and Kucherenko, Taras and Henter, Gustav Eje  and Jonas Beskow},
  booktitle=={International Conference on Intelligent Virtual Agents (IVA ’20)},
  year={2020},
  publisher = {ACM},
}

Contact

If you encounter any problems/bugs/issues please contact me on Github or by emailing me at [e-mail] for any bug reports/questions/suggestions. I prefer questions and bug reports on Github as that provides visibility to others who might be encountering same issues or who have the same questions.