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In this work, we tackle the challenging problem of learning-based single-view3D hair modeling. Due to the great difficulty of collecting paired real imageand 3D hair data, using synthetic data to provide prior knowledge for realdomain becomes a leading solution. This unfortunately introduces the challengeof domain gap. Due to the inherent difficulty of realistic hair rendering,existing methods typically use orientation maps instead of hair images as inputto bridge the gap. We firmly think an intermediate representation is essential,but we argue that orientation map using the dominant filtering-based methods issensitive to uncertain noise and far from a competent representation. Thus, wefirst 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 foundthat HairStep not only provides sufficient information for accurate 3D hairmodeling, 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. Alearning framework is further designed to transfer real images to the strandmap and depth map. It is noted that, an extra bonus of our new dataset is thefirst quantitative metric for 3D hair modeling. Our experiments show thatHairStep narrows the domain gap between synthetic and real and achievesstate-of-the-art performance on single-view 3D hair reconstruction.

HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling