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.

本文提出了一种新颖的联合对齐隐式人脸函数 (JIFF) 方法，将隐式函数与模型方法相结合，通过在 3D 形变脸模型和像素对齐 2D 特征之间进行联合预测来提高面部重建的质量，并采用粗到精的架构来预测高质量纹理。经过广泛的公共数据集的评估表明，本文提出的 JIFF 方法在定量和定性方面均优于现有的最新技术。