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Vijay Yadev
2020-08-04 19:12:31 -04:00
parent bef213dba9
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pkg/OpenFace/model_training/pdm_generation/Readme.txt Normal file
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Creating the Point Distribution Model.
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To create a model from 300W training data use the Matlab script:
./Wild_data_pdm/Create_pdm_wild.m (This might take up to a couple of hours, depending on the machine used, and if you compiled computeH - see readme in nrsfm-em folder)
You need the training data which can be acquired from (http://ibug.doc.ic.ac.uk/resources/facial-point-annotations/), to run the script from scratch. Alternatively the data is collected in 'wild_68_pts.mat', so you can skip the Collect_wild_annotations step.
The script will produce "./Wild_data_pdm/pdm_68_aligned_wild.mat" and "./Wild_data_pdm/pdm_68_aligned_wild.txt" which can be used for landmark detection.
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To create a model from Menpo + 300W training data use the Matlab script:
First convert menpo to train/validation folds (PDM training is done on training fold):
'./menpo_pdm/split_menpo_data.m'
Followed by:
Collect_menpo_annotations;
Collect_menpo_annotations_valid;
To train the actual PDM:
'./menpo_pdm/Create_pdm_wild.m' (This might take up to a couple of hours, depending on the machine used, and if you compiled computeH - see readme in nrsfm-em folder)
You need the training data which can be acquired from (https://ibug.doc.ic.ac.uk/resources/300-W/, and https://ibug.doc.ic.ac.uk/resources/2nd-facial-landmark-tracking-competition-menpo-ben/), to run the script from scratch. Alternatively the data is collected in 'menpo_68_pts.mat', so you can skip the Collect_wild_annotations step.
The script will produce "./menpo_pdm/pdm_68_aligned_menpo.mat" and "./menpo_pdm/pdm_68_aligned_menpo.txt" which can be used for landmark detection.
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As Menpo challenge expects profile faces to have chin outline, so to learn the conversion to Menpo format use "menpo_chin/learn_menpo_profile_mapping.m" (to get data you will also need to run Collect_menpo_annotations_profile_train.m, Collect_menpo_annotations_profile_valid.m)
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To visualise the results use:
visualise_PDMs.m
The PDM triangulation was created using the Delaunay triangulation algorithm, with manual hole cutting for eyes and mouth. Same can be done on any other annotated face dataset.
The pdm used in "in-the-wild" and "menpo" experiments is already included as:
./Wild_data_pdm/pdm_68_aligned_wild
./Wild_data_pdm/pdm_68_aligned_wild.txt
./menpo_pdm/pdm_68_aligned_menpo.mat
./menpo_pdm/pdm_68_aligned_menpo.txt
The same model should be generated using the Matlab script as well (overwriting the data).
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We use the non-rigid structure from motion approach by Lorenzo Torresani, Aaron Hertzmann, Chris Bregler, "Learning Non-Rigid 3D Shape from 2D Motion", NIPS 16, 2003
http://cs.stanford.edu/~ltorresa/projects/learning-nr-shape/
Please cite their work and ours if you use this code.