  Recent work on the intrinsic image of humans starts to consider the
visibility of incident illumination and encodes the light transfer function by
spherical harmonics. In this paper, we show that such a light transfer function
can be further decomposed into visibility and cosine terms related to surface
normal. Such decomposition allows us to recover the surface normal in addition
to visibility. We propose a deep learning-based approach with a reconstruction
loss for training on real-world images. Results show that compared with
previous works, the reconstruction of human face from our method better reveals
the surface normal and shading details especially around regions where
visibility effect is strong.
