Dear xiaomingLi,
You have done a great job! Thanks for sharing!
I am trying to update the face dictionary by following your code and the instructions on your paper.
However, there are a few questions need your clarification.
-
Your paper mentioned that you have used 10,000 face pics to consturct those dictionaries. I have checked your pre-trained dictionary model such as "right_eye_256_center.npy", it seems only contains 512 faces(each face feature have 128 feature map), I guess you use kmeans to select the best representative samples from 10,000 pices, is that right?
After kmeans operation, how to get the representative component? (average all feature vectors in one cluster?)
-
If I update the face dictionaries without re-train the DFDNet, the results of face reconstruction will be better or worse?
-
If my guess(1) is right, it takes huge GPU memory to store all those feature map but it only contains 500 faces. Is there a simply way to get more samples into the dictionary but remain GPU memory efficiency?
By all means, this is the best face reconstruction methods so far!
Thanks for your work, your early reply will be appreciated!
Dear xiaomingLi,
You have done a great job! Thanks for sharing!
I am trying to update the face dictionary by following your code and the instructions on your paper.
However, there are a few questions need your clarification.
Your paper mentioned that you have used 10,000 face pics to consturct those dictionaries. I have checked your pre-trained dictionary model such as "right_eye_256_center.npy", it seems only contains 512 faces(each face feature have 128 feature map), I guess you use kmeans to select the best representative samples from 10,000 pices, is that right?
After kmeans operation, how to get the representative component? (average all feature vectors in one cluster?)
If I update the face dictionaries without re-train the DFDNet, the results of face reconstruction will be better or worse?
If my guess(1) is right, it takes huge GPU memory to store all those feature map but it only contains 500 faces. Is there a simply way to get more samples into the dictionary but remain GPU memory efficiency?
By all means, this is the best face reconstruction methods so far!
Thanks for your work, your early reply will be appreciated!