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, the authors 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. They 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. The authors further extend our pipeline and introduce a coarse-to-fine architecture to predict high quality texture for their detailed face model. Extensive evaluations have been carried out on public datasets and the proposed JIFF has demonstrated superior performance (both quantitatively and qualitatively) over existing state-of-the-art.