Understanding the loss function for the optimization of the rigid transformation about tf_flame

[TimoBolkart]

Hello,
the loss itself is just the sum of squared L2 distances between the projected 2D landmarks and the target landmarks. One could also use a difference distance (including the average of absolute distances, this resembles the L1 distance). Taking the average vs summing over all landmarks is just a fixed factor which is not important, as the number of landmarks remains fixed.
The overall minimization is a weighted sum of losses, where the landmark loss is the only objective function that depends on the actual image size (all other regularizes are independent of the image size and only depend on the model dimensions). To compensate for this, we divide by some normalization factor that depends on the face size within the image. Without this normalization, the influence of the regularizes versus the landmark loss would strongly be influenced by the size of the face within the image.

[D0miH]

Ahh, I see! Thank you very much for your fast and detailed answer 👍🏼