In this work, we tackle the challenging problem of learning-based single-view
3D hair modeling. Due to the great difficulty of collecting paired real image
and 3D hair data, using synthetic data to provide prior knowledge for real
domain becomes a leading solution. This unfortunately introduces the challenge
of domain gap. Due to the inherent difficulty of realistic hair rendering,
existing methods typically use orientation maps instead of hair images as input
to bridge the gap. We firmly think an intermediate representation is essential,
but we argue that orientation map using the dominant filtering-based methods is
sensitive to uncertain noise and far from a competent representation. Thus, we
first raise this issue up and propose a novel intermediate representation,
termed as HairStep, which consists of a strand map and a depth map. It is found
that HairStep not only provides sufficient information for accurate 3D hair
modeling, but also is feasible to be inferred from real images. Specifically,
we collect a dataset of 1,250 portrait images with two types of annotations. A
learning framework is further designed to transfer real images to the strand
map and depth map. It is noted that, an extra bonus of our new dataset is the
first quantitative metric for 3D hair modeling. Our experiments show that
HairStep narrows the domain gap between synthetic and real and achieves
state-of-the-art performance on single-view 3D hair reconstruction.

本文提出了一种新的中间表示（HairStep）来减轻学习基于合成数据的单视图三维头发建模所面临的领域差距问题，并在建立了一个新的数据集进行实验，取得了最先进的性能。