Confidence Net in the model

[HOMGH]

Hey, Thanks for your great work.
Just wondering what is the notation of Confidence subnet (C-Net) in your repository? I can't find any C-Net in your codes.
I'm pretty much sure that there were R-Net and C-Net in your codes (maybe the initial version)! But now it's just R_Net and Perceptual_Net in networks.py.

[YuDeng]

Hi, sorry that the current code does not include the C-Net part. Actually, we did not release the C-Net part in any previous versions.

[HOMGH]

Thanks for your reply. So, it means you do not use Confidence Net in your model? or you've used Confidence Net in your model but haven't released it's code?!

[YuDeng]

Well, in the paper we did use the C-Net and report the evaluation results. Later in practice, we find that in most of the times C-Net only has minor improvement (in quantitative metrics) upon the naive average shape and shows no difference in visual quality. Therefore we only release the single image part (R-Net) which we think is enough to produce good result.

[HOMGH]

Gotcha! Thanks for your clarification!

[paolalf]

@YuDeng , I have a question related to this topic. I was wondering what do you mean with "naive average shape". I am trying to reconstruct a face from multiple images of the same subject and I have managed to do so by getting the face_shape_ from single images of the subject (from different angles), and then I perform the average by using np.mean() between the "face_shape_"s. This way I get a decent result, but the final face appears to be "squeezed", so I don't think that the np.mean() is what you mean with naive average shape. Thank you in advance.

[YuDeng]

Hi, the "average shape" means taking the average of identity coefficients and use the averaged coeff to construct the shape.

[paolalf]

Hi, the "average shape" means taking the average of identity coefficients and use the averaged coeff to construct the shape.

Thank you very much for your answer, @YuDeng . I have another question, If you could help me.
Assuming that I have n pictures of the same subject, if I wanted to take the average of the identity coefficients, what I understand is therefore that I have to "collect" the n coeff_s returned by every

coeff_,face_shape_,face_texture_,face_color_,landmarks_2d_,recon_img_,tri_ = sess.run([coeff,\
					face_shape,face_texture,face_color,landmarks_2d,recon_img,tri],feed_dict = {images: input_img})

performed on every image of my subject and take the average of the identity portion of said coeffs_. This is then merged with the untouched portion of the coeff_ and finally what it's get is the averaged_coeff_. Following this, do you have a suggestion on how to get the face_shape_ from the new averaged_coeff_? Do I have to call again the above code, making sure that the tf.Graph() uses the new coefficient, maybe implementing an if/else statement within the tf.Graph()? Another thing that I tried was to execute:

FaceReconstructor.Reconstruction_Block(averaged_coeff,opt)
face_shape = FaceReconstructor.face_shape_t

within the tf.Session(), but it doesn't seem to be the right path to follow.
Thank you again for your time.