VisionSystemsInc / pix2face_net

pytorch implementation of "pix2face" network for 3D face estimation from 2D images

pix2face_net

Note: For a complete 3D face geometry estimation and rendering solution with documentation, see pix2face, which contains this repository as a submodule.

Direct Estimation of 3D Face Pose and Geometry from 2D Images

Requirements

• pytorch

• numpy

• scikit-image

• Training Data

  You will need three sets of training images: Input, PNCC, and offsets.

  • Input: The input RGB face image.

  • PNCC: "Projected Normalized Coordinate Code", as described in [1]

  • Offsets: 3D offsets from the "mean face" position to the observed 3D position.

[1] X. Zhu, Z. Lei, X. Liu, H. Shi, and S. Z. Li, “Face Alignment Across Large Poses: A 3D Solution”, CVPR 2016.

Training

python train.py --input_dir $INPUT_DIR --PNCC_dir $PNCC_DIR --offsets_dir $OFFSETS_DIR \
--val_input_dir $VAL_INPUT_DIR --val_PNCC_dir $VAL_PNCC_DIR --val_offsets_dir $VAL_OFFSETS_DIR \
--output_dir $OUTPUT_DIR

Testing

python test.py --model $OUTPUT_DIR/pix2face_unet.pth \
--input <image_or_directory> --output_dir <output_dir>

Demo

See demo.py for an example of a transformation from image --> PNCC + offsets --> 3D Point Cloud.

In order to run the demo, you will need to train the network or download a pre-trained model.

Citation

If you find this software useful, please consider referencing:

@INPROCEEDINGS{pix2face2017,
author = {Daniel Crispell and Maxim Bazik},
booktitle = {2017 IEEE International Conference on Computer Vision Workshop (ICCVW)},
title = {Pix2Face: Direct 3D Face Model Estimation},
year = {2017},
pages = {2512-2518},
ISSN = {2473-9944},
month={Oct.}
}

Contact

Daniel Crispell [e-mail]