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

Our paper seeks to transfer the hairstyle of a reference image to an input
photo for virtual hair try-on. We target a variety of challenges scenarios,
such as transforming a long hairstyle with bangs to a pixie cut, which requires
removing the existing hair and inferring how the forehead would look, or
transferring partially visible hair from a hat-wearing person in a different
pose. Past solutions leverage StyleGAN for hallucinating any missing parts and
producing a seamless face-hair composite through so-called GAN inversion or
projection. However, there remains a challenge in controlling the
hallucinations to accurately transfer hairstyle and preserve the face shape and
identity of the input. To overcome this, we propose a multi-view optimization
framework that uses "two different views" of reference composites to
semantically guide occluded or ambiguous regions. Our optimization shares
information between two poses, which allows us to produce high fidelity and
realistic results from incomplete references. Our framework produces
high-quality results and outperforms prior work in a user study that consists
of significantly more challenging hair transfer scenarios than previously
studied. Project page: this https URL

本论文提出了一种基于多视图优化的方法，结合 StyleGAN 技术进行头发的虚拟试穿，能够准确地转移发型并保留输入图片的面部特征和身份信息。

StyleGAN 沙龙：多视角潜空间优化实现姿态不变发型转移

StyleGAN Salon: Multi-View Latent Optimization for Pose-Invariant  Hairstyle Transfer

Automated capture of animal pose is transforming how we study neuroscience
and social behavior. Movements carry important social cues, but current methods
are not able to robustly estimate pose and shape of animals, particularly for
social animals such as birds, which are often occluded by each other and
objects in the environment. To address this problem, we first introduce a model
and multi-view optimization approach, which we use to capture the unique shape
and pose space displayed by live birds. We then introduce a pipeline and
experiments for keypoint, mask, pose, and shape regression that recovers
accurate avian postures from single views. Finally, we provide extensive
multi-view keypoint and mask annotations collected from a group of 15 social
birds housed together in an outdoor aviary. The project website with videos,
results, code, mesh model, and the Penn Aviary Dataset can be found at
this https URL

自动捕捉动物姿态正在改变我们对于神经科学和社交行为的学习，本文提出了一种模型和多视角优化方法，用于捕捉鸟类的独特形状和姿态，并引入了一个管道和实验，实现了从单个视角恢复精确的鸟类姿态的关键点、掩码、姿势和形状回归。