Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

use implicit keypoints equivariance loss LE,这里训练的GT是怎么获取到的? #383

Open
zdyshine opened this issue Sep 11, 2024 · 1 comment

Comments

@zdyshine
Copy link

zdyshine commented Sep 11, 2024

感谢分享的工作,想请问训练的gt是怎么获取的?

@zzzweakman
Copy link
Collaborator

您好,您可以查阅Face Vid2Vid论文的3.4节,或者在它的补充材料中找到更详细的说明,下面是关于L_E的描述:This loss ensures the consistency of image-specific keypoints xd,k. For a valid keypoint, when applying a 2D transformation to the image, the predicted keypoints should change according to the applied transformation. Since we predict 3D instead of 2D keypoints, We use an orthographic projection to project the keypoints to the image plane before computing the loss.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Labels
None yet
Projects
None yet
Development

No branches or pull requests

2 participants