Comments (1)
This is somehow a common trick when playing with attention map / attention mask. From my point of view,
- Intuitively attention mask enables some specific areas where facial muscle changed to get more focus, applying it to the color mask (
) can generate images with clear dynamic area and unclear static area.
- After that, what left is to enhance the static area, which should be similar between the generated image and the original real image. So we can enhance the static area (basically it refers to background area) in the original real image (
) and merge it to the above to get final result (
).
You can understand it more clearly with the help of Sec. 4.1_Generator and Sec. 6.5 of the original paper.
However, the above is what the paper tells, while this project actually implements it in a different (or wrong?) way. You can check out Issue #21 for more details.
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Related Issues (20)
- Is this how you would run your sample images ? HOT 2
- TypeError: iteration over a 0-d array
- Problem with Preparing annotation
- Pretrained Openface HOT 3
- About the paper figure
- A question about some loss functions in the paper
- Give “Attention” a name.
- Suggestion of using AU R-CNN instead of OpenFace for better AU detection accuracy.
- tensors must have same number of dimensions: got 2 and 3
- Attention Loss
- TypeError: Cannot handle this data type HOT 1
- when training “the loss_d_real is negative value ” is OK? and why
- Generator is different from that in paper HOT 1
- AUs as input parameter for trained model HOT 1
- IndexError: During training 0-dim tensor error HOT 2
- Can you share you train data in this paper? HOT 1
- Pretrained Model HOT 2
- Demo??
- Why the c_dim is 5 HOT 1
- How to get tar_aus in the batch ,what is the tar_aus?
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