This is the official PyTorch implementation of Face Editing Using Part-Based Optimization of the Latent Space. Please cite this paper if you use this code in your own work. Please also let us know.

@article{https://doi.org/10.1111/cgf.14760,
author = {Aliari, Mohammad Amin and Beauchamp, Andre and Popa, Tiberiu and Paquette, Eric},
title = {Face Editing Using Part-Based Optimization of the Latent Space},
journal = {Computer Graphics Forum},
volume = {42},
number = {2},
pages = {269-279},
keywords = {CCS Concepts, • Computing methodologies → Mesh models, Neural networks},
doi = {https://doi.org/10.1111/cgf.14760},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/cgf.14760},
eprint = {https://onlinelibrary.wiley.com/doi/pdf/10.1111/cgf.14760},
year = {2023}
}

1. Install requirements.txt.

   • For installing psbody_mesh on Windows, follow this link. We use version 0.3.

2. Place the meshes in a directory with names following this format: face (1).obj to face (i).obj.

3. Open src/face_exctractor.py:

   • Set the heads_path to the dir mentioned in (2) in src/face_exctractor.py.
   • Run src/face_exctractor.py.

4. Open src/part_exctractor.py:

   • Set the heads_path to the dir mentioned in (2) in src/part_exctractor.py.
   • Run src/part_exctractor.py.

5. Open and follow the steps in src/train.py or src/train.ipynbpy to train the model.

6. Open src/face_editor.py:

   • Set model_path to the newly trained model.

7. The face editor can be launched now:

   • python src/face_editor.py

The model can be trained with the FaceWarehouse dataset. It can be acquired by following the steps described here.

• Model name: NEURAL_FACE

  • Path: src/coma/models/neural_face.py

• src/face_editor.py:

  • It is an environment to edit and test the faces.
  • It supports texturing by passing viewer.add_mesh the texture image path.
    • toggle_texture() can be called to show the textures.

• Customizable files: The following files in the /data folder are hand generated and customizable:

  1. landmarks.json: Face landmarks. These are the editing points.

  2. measures.json: These are the defined measurements.

     • We have different measures like nose width, lips height, etc.
     • New ones can easily be defined. They can also bed used with any newly defined landmarks.
     • We also have a number of equations, and new ones can be defined in util_control.py.

  3. face.csv and parts_info files:

     • These are our defined face areas and segmentations.
     • Most DCC tool can be used to select other sets of vertices for each segment.
     • Beware that some DCC tools start the vertex index from 1 and others from 0. This might introduce some bugs in runtime. Controller.do_minus_one can be set to fix the issue.

• Ranjan, A., Bolkart, T., Sanyal, S., & Black, M. J. (2018). Generating 3D faces using convolutional mesh autoencoders. Proceedings of the European Conference on Computer Vision (ECCV), 704–720.
• C. Cao, Y. Weng, S. Zhou, Y. Tong and K. Zhou, "FaceWarehouse: A 3D Facial Expression Database for Visual Computing," in IEEE Transactions on Visualization and Computer Graphics, vol. 20, no. 3, pp. 413-425, March 2014, doi: 10.1109/TVCG.2013.249.

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