  We propose a robust and accurate non-parametric method for single-view 3D
face reconstruction (SVFR). While tremendous efforts have been devoted to
parametric SVFR, a visible gap still lies between the result 3D shape and the
ground truth. We believe there are two major obstacles: 1) the representation
of the parametric model is limited to a certain face database; 2) 2D images and
3D shapes in the fitted datasets are distinctly misaligned. To resolve these
issues, a large-scale pseudo 2D\&3D dataset is created by first rendering the
detailed 3D faces, then swapping the face in the wild images with the rendered
face. These pseudo 2D&3D pairs are created from publicly available datasets
which eliminate the gaps between 2D and 3D data while covering diverse
appearances, poses, scenes, and illumination. We further propose a
non-parametric scheme to learn a well-generalized SVFR model from the created
dataset, and the proposed hierarchical signed distance function turns out to be
effective in predicting middle-scale and small-scale 3D facial geometry. Our
model outperforms previous methods on FaceScape-wild/lab and MICC benchmarks
and is well generalized to various appearances, poses, expressions, and
in-the-wild environments. The code is released at
http://github.com/zhuhao-nju/rafare .
