  This paper addresses the problem of single view 3D human reconstruction.
Recent implicit function based methods have shown impressive results, but they
fail to recover fine face details in their reconstructions. This largely
degrades user experience in applications like 3D telepresence. In this paper,
we focus on improving the quality of face in the reconstruction and propose a
novel Jointly-aligned Implicit Face Function (JIFF) that combines the merits of
the implicit function based approach and model based approach. We employ a 3D
morphable face model as our shape prior and compute space-aligned 3D features
that capture detailed face geometry information. Such space-aligned 3D features
are combined with pixel-aligned 2D features to jointly predict an implicit face
function for high quality face reconstruction. We further extend our pipeline
and introduce a coarse-to-fine architecture to predict high quality texture for
our detailed face model. Extensive evaluations have been carried out on public
datasets and our proposed JIFF has demonstrates superior performance (both
quantitatively and qualitatively) over existing state-of-the-arts.
