  This paper presents a neural network to estimate a detailed depth map of the
foreground human in a single RGB image. The result captures geometry details
such as cloth wrinkles, which are important in visualization applications. To
achieve this goal, we separate the depth map into a smooth base shape and a
residual detail shape and design a network with two branches to regress them
respectively. We design a training strategy to ensure both base and detail
shapes can be faithfully learned by the corresponding network branches.
Furthermore, we introduce a novel network layer to fuse a rough depth map and
surface normals to further improve the final result. Quantitative comparison
with fused `ground truth' captured by real depth cameras and qualitative
examples on unconstrained Internet images demonstrate the strength of the
proposed method. The code is available at
https://github.com/sfu-gruvi-3dv/deep_human.
