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

JIFF: Jointly-aligned Implicit Face Function for High Quality Single View Clothed Human Reconstruction