Undoubtedly, high-fidelity 3D hair is crucial for achieving realism, artistic
expression, and immersion in computer graphics. While existing 3D hair modeling
methods have achieved impressive performance, the challenge of achieving
high-quality hair reconstruction persists: they either require strict capture
conditions, making practical applications difficult, or heavily rely on learned
prior data, obscuring fine-grained details in images. To address these
challenges, we propose MonoHair,a generic framework to achieve high-fidelity
hair reconstruction from a monocular video, without specific requirements for
environments. Our approach bifurcates the hair modeling process into two main
stages: precise exterior reconstruction and interior structure inference. The
exterior is meticulously crafted using our Patch-based Multi-View Optimization
(PMVO). This method strategically collects and integrates hair information from
multiple views, independent of prior data, to produce a high-fidelity exterior
3D line map. This map not only captures intricate details but also facilitates
the inference of the hair's inner structure. For the interior, we employ a
data-driven, multi-view 3D hair reconstruction method. This method utilizes 2D
structural renderings derived from the reconstructed exterior, mirroring the
synthetic 2D inputs used during training. This alignment effectively bridges
the domain gap between our training data and real-world data, thereby enhancing
the accuracy and reliability of our interior structure inference. Lastly, we
generate a strand model and resolve the directional ambiguity by our hair
growth algorithm. Our experiments demonstrate that our method exhibits
robustness across diverse hairstyles and achieves state-of-the-art performance.
For more results, please refer to our project page
this https URL

通过提出 MonoHair 框架，我们能实现高度逼真的 3D 头发重建，同时解决了严格捕捉条件或依赖先验数据带来的问题。我们的方法将模型分为外部和内部重建两个阶段，并利用 Patch-based Multi-View Optimization（PMVO）和多视图 3D 头发重建等技术来提高重建效果。实验证明了我们方法在不同发型上的鲁棒性和卓越性能。

MonoHair：从单眼视频中实现高保真发型建模

MonoHair: High-Fidelity Hair Modeling from a Monocular Video

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。

HairStep：使用股和深度图将合成的头发转移到真实头发，用于单视图 3D 头发建模

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

We present sketchhair, a deep learning based tool for interactive modeling of
3D hair from 2D sketches. Given a 3D bust model as reference, our sketching
system takes as input a user-drawn sketch (consisting of hair contour and a few
strokes indicating the hair growing direction within a hair region), and
automatically generates a 3D hair model, which matches the input sketch both
globally and locally. The key enablers of our system are two carefully designed
neural networks, namely, S2ONet, which converts an input sketch to a dense 2D
hair orientation field; and O2VNet, which maps the 2D orientation field to a 3D
vector field. Our system also supports hair editing with additional sketches in
new views. This is enabled by another deep neural network, V2VNet, which
updates the 3D vector field with respect to the new sketches. All the three
networks are trained with synthetic data generated from a 3D hairstyle
database. We demonstrate the effectiveness and expressiveness of our tool using
a variety of hairstyles and also compare our method with prior art.

Sketchhair 是一项基于深度学习技术的 3D 头发交互建模工具，它使用两个精心设计的神经网络（S2ONet 和 O2VNet）将用户手绘的草图转换为 3D 头发模型，还支持使用附加草图进行头发编辑。