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FLAME: Articulated Expressive 3D Head Model

This is an official FLAME repository.

We also provide Tensorflow FLAME and PyTorch FLAME frameworks, and code to convert from Basel Face Model to FLAME.

FLAME is a lightweight and expressive generic head model learned from over 33,000 of accurately aligned 3D scans. FLAME combines a linear identity shape space (trained from head scans of 3800 subjects) with an articulated neck, jaw, and eyeballs, pose-dependent corrective blendshapes, and additional global expression blendshapes. For details please see the scientific publication

Learning a model of facial shape and expression from 4D scans
Tianye Li*, Timo Bolkart*, Michael J. Black, Hao Li, and Javier Romero
ACM Transactions on Graphics (Proc. SIGGRAPH Asia) 2017

and the supplementary video.

This codebase demonstrates

• Sampling: Load and evaluate FLAME model for random parameters
• Landmark fitting: Fit FLAME to 3D landmarks
• Scan fitting: Fit FLAME to a 3D scan

Set-up

The code has been tested with Python 3.6.9.

Clone the git project:

git clone https://github.com/Rubikplayer/flame-fitting.git

Install pip and virtualenv

sudo apt-get install python3-pip python3-venv

Set up virtual environment:

mkdir <your_home_dir>/.virtualenvs
python3 -m venv <your_home_dir>/.virtualenvs/flame-fitting

Activate virtual environment:

cd flame-fitting
source <your_home_dir>/.virtualenvs/flame-fitting/bin/activate

Make sure your pip version is up-to-date:

pip install -U pip

Some requirements can be installed using:

pip install -r requirements.txt

Install mesh processing libraries from MPI-IS/mesh within the virtual environment.

The scan-to-mesh distance used for fitting a scan depends on Eigen. Either download Eigen from here OR clone the repository:

git clone https://gitlab.com/libeigen/eigen.git

After downloading Eigen, you need to compile the code in the directory 'sbody/alignment/mesh_distance'. To do this go to the directory:

cd sbody/alignment/mesh_distance

Edit the file setup.py to set EIGEN_DIR to the location of Eigen. Then type:

make

Data

To download the FLAME model, sign up and agree to the model license under MPI-IS/FLAME. Then run following script:

./fetch_FLAME.sh

Demo

• Load and evaluate FLAME model: hello_world.py
• Fit FLAME to 3D landmarks: fit_lmk3d.py
• Fit FLAME to a 3D scan: fit_scan.py

Fitting a scan requires scan and FLAME model to be in the same local coordiante systems. The fit_scan.py script provides different options by specifying the variable scale_unit to convert from Meters [m] (default), Centimeters [cm], or Milimieters [mm]. Please specify the right unit when running fit_scan.py. If the unit of the measurement unit is unknown, choose scale_unit = 'NA'.

Landmarks

The provided demos fit FLAME to 3D landmarks or to a scan, using 3D landmarks for initialization and during fitting. Both demos use the shown 51 landmarks. Providing the landmarks in the exact order is essential. The landmarks can for instance be obtained with MeshLab using the PickPoints module. PickPoints outputs a .pp file containing the selected points. The .pp file can be loaded with the provided 'load_picked_points(fname)' function in fitting/landmarks.py.

Citing

When using this code in a scientific publication, please cite FLAME

@article{FLAME:SiggraphAsia2017,
  title = {Learning a model of facial shape and expression from {4D} scans},
  author = {Li, Tianye and Bolkart, Timo and Black, Michael. J. and Li, Hao and Romero, Javier},
  journal = {ACM Transactions on Graphics, (Proc. SIGGRAPH Asia)},
  volume = {36},
  number = {6},
  year = {2017},
  url = {https://doi.org/10.1145/3130800.3130813}
}

License

The FLAME model is under a Creative Commons Attribution license. By using this code, you acknowledge that you have read the terms and conditions (https://flame.is.tue.mpg.de/modellicense.html), understand them, and agree to be bound by them. If you do not agree with these terms and conditions, you must not use the code. You further agree to cite the FLAME paper when reporting results with this model.

Supported projects

Visit the FLAME-Universe for an overview of FLAME-based projects.

FLAME supports several projects such as

• CoMA: Convolutional Mesh Autoencoders
• RingNet: 3D Face Shape and Expression Reconstruction from an Image without 3D Supervision
• VOCA: Voice Operated Character Animation
• Expressive Body Capture: 3D Hands, Face, and Body from a Single Image
• ExPose: Monocular Expressive Body Regression through Body-Driven Attention
• GIF: Generative Interpretable Faces
• DECA: Detailed Expression Capture and Animation

FLAME is part of SMPL-X: : A new joint 3D model of the human body, face and hands together.

Acknowledgement

Code in smpl_webuser originates from SMPL Python code, and code in sbody originates from SMALR. We thank the authors for pushing these code packages.