Rescale factor and similarity_Lm3D_all.mat about deep3dfacerecon_pytorch

[YuDeng]

The rescale factor is to ensure that faces in images have a proper scale. Our model is trained using this scale so if you change it the performance might drop.

The similarity_Lm3D_all.mat contains 68 3D landmarks of the BFM2009 mean shape. We use them to align the size of faces in different images.

[humannumber196]

The rescale factor is to ensure that faces in images have a proper scale. Our model is trained using this scale so if you change it the performance might drop.

The similarity_Lm3D_all.mat contains 68 3D landmarks of the BFM2009 mean shape. We use them to align the size of faces in different images.

Thanks for your reply, Yu! I tried to extract the 68 3D landmarks from the mean shape, but the coordinates value are very large, it seems you normalized them? I used the 68 landmarks indices from https://github.com/anilbas/BFMLandmarks and tried to normalize them, but the results are still different from yours. Could you please tell me how did you normalize them or how did you get these values?

[YuDeng]

Yes, we rescale the BFM meanshape. For details you can check https://github.com/sicxu/Deep3DFaceRecon_pytorch/blob/master/util/load_mats.py.
After the preprocessing, we extract the 68 landmarks using the indices in facemodel_info.mat. We get the keypoints indices information from https://github.com/Juyong/3DFace.

[humannumber196]

Yes, we rescale the BFM meanshape. For details you can check https://github.com/sicxu/Deep3DFaceRecon_pytorch/blob/master/util/load_mats.py.
After the preprocessing, we extract the 68 landmarks using the indices in facemodel_info.mat. We get the keypoints indices information from https://github.com/Juyong/3DFace.

OK. Thanks a lot, Yu!

[giracle]

Hi, Thanks for sharing your code, it is a great work! But I have a question about it. How do you normalize the 3D landmarks? Thank you.