  In 3D face reconstruction, orthogonal projection has been widely employed to
substitute perspective projection to simplify the fitting process. This
approximation performs well when the distance between camera and face is far
enough. However, in some scenarios that the face is very close to camera or
moving along the camera axis, the methods suffer from the inaccurate
reconstruction and unstable temporal fitting due to the distortion under the
perspective projection. In this paper, we aim to address the problem of
single-image 3D face reconstruction under perspective projection. Specifically,
a deep neural network, Perspective Network (PerspNet), is proposed to
simultaneously reconstruct 3D face shape in canonical space and learn the
correspondence between 2D pixels and 3D points, by which the 6DoF (6 Degrees of
Freedom) face pose can be estimated to represent perspective projection.
Besides, we contribute a large ARKitFace dataset to enable the training and
evaluation of 3D face reconstruction solutions under the scenarios of
perspective projection, which has 902,724 2D facial images with ground-truth 3D
face mesh and annotated 6DoF pose parameters. Experimental results show that
our approach outperforms current state-of-the-art methods by a significant
margin. The code and data are available at
https://github.com/cbsropenproject/6dof_face.
