Position Map Regression Networks (PRN) is a method to jointly regress dense alignment and 3D face shape in an end-to-end manner. In this article, I’ll provide a short explanation and discuss its applications in computer vision.

When I was a child, I imagined that (due to movies, of course), in the future, we’d be able to have these crazy holograms where you could see people talking to you as if they were there. These kinds of applications for computer vision suggest we aren’t that far from achieving something similar.

In the last few decades, a lot of important research groups in computer vision have made amazing advances in 3D face reconstruction and face alignment. Primarily, these groups have used CNNs as the de facto ANN for this task. However, the performance of these methods is restricted because of the limitations of 3D space defined by face model templates used for mapping.

Position Map Regression Networks (PRN)