Comments (3)
YingqingHe
commented on June 19, 2024
Hi.
(1) Our method does not need to find the shadow part in the latent space. Instead, we aim to find the latent vector which represent the clean face with no shadow. In order to do this, we propose a 3-stage optimization process to get the latent vector only for the shadow-free face (which details can be found in our paper).
(2) In stage1, we create 500 latent vectors only for a better initialization of the latent vector, and we choose a best one which generated image has the lowest perceptural loss with the input image.
(3) Actually our method is not based on manipulation in latent space. We explicitly model the shadow generation process via the color matrix and the shadow mask. More details can be checked in our paper. Thanks!
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SleyAI
commented on June 19, 2024
Hello, thank you very much for your reply! Can you explain how you receive the Initial shadow free image after step 1? In your optimization process in step 1 you calculate the LPIPS loss between the generated images and the original image (which contains shadows). How can you generate a shadow free image by comparing with a shadowed image?
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YingqingHe
commented on June 19, 2024
Hi，
Just by early stopping.
In step 1 we only want to rough version of face, so just stop optimization
of latent code after a few of steps.
We can obtain a more precise clean face(step 3) after the shadow has been
learned(step 2).
Best,
Yingqing
Henning ***@***.***>于2022年1月19日 周三21:55写道：
… Hello, thank you very much for your reply! Can you explain how you receive
the Initial shadow free image after step 1? In your optimization process in
step 1 you calculate the LPIPS loss between the generated images and the
original image (which contains shadows). How can you generate a shadow free
image by comparing with a shadowed image?
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