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Thank you for your great contributions to the 3D face alignment field. I am currently working on a project related to facial occlusion and would greatly benefit from the AFLW2000-3D-occlusion dataset.…
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For benchmark evolution on AFLW2000:
1) In the Table 1 of paper, the reported numbers are evaluated using (x, y) without z ?
1> Evaluation number by your given model hints on that way
2> Th…
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Where to obtain “../SynergyNet/aflw2000_data/eval/ALFW2000-3D_pose_3ANG_excl.npy” and '../SynergyNet/aflw2000_data/eval/ALFW2000-3D_pose_3ANG_skip.npy'
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Validation is done in 1969 images of AFLW2000 (not 2000 in paper).
KeyKy updated
2 years ago
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Hi!@natanielruiz, Great papers and work!
I have a question about data preprocessing, I performed well on the training set, but poorly on the test set.
Since I am training a small network, I will l…
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My 5 landmarks come from GT and use pre-trained model FaceReconModel.pb, but they are far from the author's performance. why？Is there any way to improve？Thank you~
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PRNet has different dimensions than any other model because the neck part is removed. When I evaluated PRNet and your model, I found that PRNet's has the worst NME, probably because I removed some dim…