In this paper we propose to learn a mapping from image
pixels into a dense template grid through a fully convolutional
network. We formulate this task as a regression problem
and train our network by leveraging upon manually annotated
facial landmarks “in-the-wild”. We use such landmarks
to establish a dense correspondence field between
a three-dimensional object template and the input image,
which then serves as the ground-truth for training our regression
system. We show that we can combine ideas from
semantic segmentation with regression networks, yielding a
highly-accurate ‘quantized regression’ architecture.
Our system, called DenseReg, allows us to estimate
dense image-to-template correspondences in a fully convolutional
manner. As such our network can provide useful
correspondence information as a stand-alone system, while
when used as an initialization for Statistical Deformable
Models we obtain landmark localization results that largely
outperform the current state-of-the-art on the challenging
300W benchmark. We thoroughly evaluate our method on
a host of facial analysis tasks, and demonstrate its use for
other correspondence estimation tasks, such as the human
body and the human ear. DenseReg code is made available
at http://alpguler.com/DenseReg.html along
with supplementary materials.