CVPR 2023, highlight paper - HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling

In this episode we discuss HairStep: Transfer Synthetic to Real Using Strand and Depth Maps for Single-View 3D Hair Modeling by Yujian Zheng, Zirong Jin, Moran Li, Haibin Huang, Chongyang Ma, Shuguang Cui, Xiaoguang Han. The paper discusses the problem of learning-based single-view 3D hair modelling, which has difficulties in collecting real image and 3D hair data. Using synthetic data as prior knowledge for real domain introduces a challenge of domain gap, which can be bridged using orientation maps instead of hair images as input. However, existing methods using orientation maps are sensitive to noise and far from a competent representation. Therefore, the paper proposes a novel intermediate representation called HairStep, which consists of a strand map and a depth map, providing sufficient information for accurate 3D hair modelling and feasible to be inferred from real images. The proposed approach achieves state-of-the-art performance on single-view 3D hair reconstruction.