24 lines
1.4 KiB
Text
24 lines
1.4 KiB
Text
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Creating the Point Distribution Model.
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To create a model from wild data use the Matlab script:
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./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)
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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.
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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 visualise the results use:
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visualise_PDMs.m
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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.
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The pdm used in "in-the-wild" experiments is already included as:
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./Wild_data_pdm/pdm_68_aligned_wild
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./Wild_data_pdm/pdm_68_aligned_wild.txt
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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
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http://cs.stanford.edu/~ltorresa/projects/learning-nr-shape/
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Please cite their work and ours if you use this code.
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