NeuralHDHair: High-Fidelity Hair Modeling from a Single Image

[Submitted on 9 May 2022]

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Abstract: 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.

Submission history From: Keyu Wu [view email]

[v1]
Mon, 9 May 2022 10:39:39 UTC (9,274 KB)

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