How to make network learn about shape and color of the object separately?

I saw the paper and code of a Person Reidentification library by NVIDIA - GitHub - NVlabs/DG-Net: Joint Discriminative and Generative Learning for Person Re-identification. CVPR'19 (Oral)

It says there are two different network to focus on Person body shape and clothing separately.

Sorry if the question is noobish.

Intuitively I would run the image through an edge detector, and then train on that image to make the network learn the structure of the pedestrian. - To make it focus on the shape

And the network which emphasizes on the color of clothing would be fed the original RGB image instead of greyed one.

I skimmed through the code, but I didnt find any snippet where I can see the above.

Is my above assumption of Edge detector and RGB correct? Or the NN takes care of this? If yes, then how?