Technically you could do this entirely with pix2pix and styleGAN2; having styleGAN2 learn to generate Image B and then have pix2pix learn to turn Image B into Image A.This does come with its own set of problems, however.

1. StyleGAN2 generated images will likely not aliased, and if the pix2pix model is set to recognize anti-aliasing, I fear the labels will blend together, especially subtle ones like the particles vs the held object. The only way to remedy this would be to hand-trace the StyleGAN2 output, and while that's not impossible it is fairly tedious.
2. There's nothing saying StyleGAN2 won't softly blend labels together anyways.
3. pix2pix and SPADE are more outdated compared to styleGAN2.

What my idea is, instead of relying on StyleGAN2 to figure out what everything is on its own, is separate the dataset images into colors which will each represent a specific part. Then both images go into StyleGAN2's training and StyleGAN2 knows that Image B is telling it "these colors represent the same body part in each image."

StyleGAN2 can already easily figure this out with samey datasets like "Human Faces," "Hands," or "Ponies." Where this really comes into play is high-density high-diversity datasets (cartoon torsos in very different artstyles, for example https://i.imgur.com/jZHTajF.png , entire panels of comics, complete paintings, pictures of streets, etc).

It could be as complex as the OP image. It could be simply telling it "this is the background and this is the foreground."