  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
https://keyuwu-cs.github.io/MonoHair/.
