Repository for the paper - A lightweight 3D dense facial landmark estimation model from position map data
Prepare Data -
Follow the instruction in face3d page to generate the UV map data from the 300W-LP dataset.
Make sure to install mesh_core_cython in the local python environment by running -
# directory - face3d/mesh/cython
pip install cython
python3 setup.py build_ext --inplace
python3 setup.py install- Point the BFM.mat and BFM_UV.mat in generate_posmap_300WLP.py
- Update the input_path and output_path in generate_posmap_300WLP.py
Training -
run - train_mobilnet.py
Inference -
run - inference.py
Pretrained Checkpoint -
https://drive.google.com/file/d/1pfUZRMzLh8m53RI3mOqbjDfJGeOZHE_i/view?usp=sharing
Check the Configs/config.py for the configuration details.
Sample Results -
If you use this code, please consider citing:
@article{basak2023lightweight,
title={A lightweight 3D dense facial landmark estimation model from position map data},
author={Basak, Shubhajit and Mangapuram, Sathish and Costache, Gabriel and McDonnell, Rachel and Schukat, Michael},
journal={arXiv preprint arXiv:2308.15170},
year={2023}
}