I have implemented population analysis on my dataset which consists of 8 muscles of the same type and I got the mean shape and principal components.

Obviously, I can change the slider according to the table(PCA projection table) to transform the shape (PCA exploration) into one of the muscles of the dataset.

And my question is If I have a new muscle, how can I get the corresponding weights for each component(like the PCA projection table) to convert the shape(PCA exploration) to the new muscle. This is the last step of my Group project

It appears you are adding a new muscle set to your data. That could mean one more degree of freedom. Or you could use the muscle components you have now (8) to describe each new muscle.
One step involves making the pca again, the other using what you have with some registration algorithm

Sorry my problem description may not be clear enough.

Actually, the dataset consists of 10 muscles of the same type. And before the population analysis, I chose 2 of the 10 muscles to use as a validation set to evaluate the generalization of the result of population analysis(whether I can use the mean shape and principal components to fit the validation muscles). Then I implement the population analysis on the remaining 8 muscles.

As shown below, I can get the mean shape(the purple 3D model) to fit any one of the 8 muscles(the yellow 3D model) by adjusting the sliders of pc1 and pc2 according the “PCA projection table”(Right side of picture).

Theoretically, I can indeed fit any muscles of the same type by adjusting the weight of the principal components. And My question is how to get the corresponding weights of components for muscles of the validation set just like the result “PCA projection table”.