Joint 3D Face Reconstruction and Dense
Alignment with Position Map Regression
Network
Abstract. We propose a straightforward method that simultaneously
reconstructs the 3D facial structure and provides dense alignment. To
achieve this, we design a 2D representation called UV position map which
records the 3D shape of a complete face in UV space, then train a simple Convolutional Neural Network to regress it from a single 2D image.
We also integrate a weight mask into the loss function during training
to improve the performance of the network. Our method does not rely
on any prior face model, and can reconstruct full facial geometry along
with semantic meaning. Meanwhile, our network is very light-weighted
and spends only 9.8ms to process an image, which is extremely faster
than previous works. Experiments on multiple challenging datasets show
that our method surpasses other state-of-the-art methods on both reconstruction and alignment tasks by a large margin. Code is available at
https://github.com/YadiraF/PRNet