Undoubtedly, high-fidelity 3D hair plays an indispensable role in digital
humans. However, existing monocular hair modeling methods are either tricky to
deploy in digital systems (e.g., due to their dependence on complex user
interactions or large databases) or can produce only a coarse geometry. In this
paper, we introduce NeuralHDHair, a flexible, fully automatic system for
modeling high-fidelity hair from a single image. The key enablers of our system
are two carefully designed neural networks: an IRHairNet (Implicit
representation for hair using neural network) for inferring high-fidelity 3D
hair geometric features (3D orientation field and 3D occupancy field)
hierarchically and a GrowingNet(Growing hair strands using neural network) to
efficiently generate 3D hair strands in parallel. Specifically, we perform a
coarse-to-fine manner and propose a novel voxel-aligned implicit function
(VIFu) to represent the global hair feature, which is further enhanced by the
local details extracted from a hair luminance map. To improve the efficiency of
a traditional hair growth algorithm, we adopt a local neural implicit function
to grow strands based on the estimated 3D hair geometric features. Extensive
experiments show that our method is capable of constructing a high-fidelity 3D
hair model from a single image, both efficiently and effectively, and achieves
the-state-of-the-art performance.

本文介绍了一种使用神经网络从单一图像中建立高保真度的 3D 头发模型的方法，通过使用两种不同的神经网络，IRHairNet 和 GrowingNet，来实现头发几何特征的推测和 3D 头发丝的生成，从而取得了最先进的性能表现。