  3D face reconstruction and face alignment are two fundamental and highly
related topics in computer vision. Recently, some works start to use deep
learning models to estimate the 3DMM coefficients to reconstruct 3D face
geometry. However, the performance is restricted due to the limitation of the
pre-defined face templates. To address this problem, some end-to-end methods,
which can completely bypass the calculation of 3DMM coefficients, are proposed
and attract much attention. In this report, we introduce and analyse three
state-of-the-art methods in 3D face reconstruction and face alignment. Some
potential improvement on PRN are proposed to further enhance its accuracy and
speed.
