  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 also provide qualitative results for dense human body
correspondence. We make our code available at http://alpguler.com/DenseReg.html
along with supplementary materials.
