Comments (9)
Please see the explanations about inverse warping here:
https://www.cs.unc.edu/~lazebnik/research/fall08/lec08_faces.pdf
this should help you understand!
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Also your paper said that "A keypoint is considered to be matched correctly if its predicted location is within a distance of α · max(h, w) of the target keypoint position". So i don't know why you code compare with source points instead of target points.
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Thank for you reply :)
So, it means that, i just replace "source points" and "target point" in the code and got the natural result ?.
But it too weird for me...
Because in the code: You warped source images -> target images, and using theta result to get inverse warping...
So, could you tell me, how to get target point from source points and theta result ?
Thank you !
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so do you understand his means? I'm also confused this opinions.
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@lixiaolusunshine yes, i understood him. Clearly that, the paper said that source points to target points, but in the source code is totally inverse.
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so in his paper he got the estimated inverse affine parameters from the featuregression layer, then use this inverse mapping to warp the source image into the target image?
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@lixiaolusunshine sorry, i cannot catch up your mean. In his paper, very clear that, use GMM, find a list of parameters, from parameters => warp => loss.
The only difference is when he compare the result. He compare the target points, but in code, we never get target points for the parameters, instead of is source points.
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Related Issues (20)
- datasets
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- Training with affine AND TPS HOT 1
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