Generating realistic human 3D reconstructions using image or video data is
essential for various communication and entertainment applications. While
existing methods achieved impressive results for body and facial regions,
realistic hair modeling still remains challenging due to its high mechanical
complexity. This work proposes an approach capable of accurate hair geometry
reconstruction at a strand level from a monocular video or multi-view images
captured in uncontrolled lighting conditions. Our method has two stages, with
the first stage performing joint reconstruction of coarse hair and bust shapes
and hair orientation using implicit volumetric representations. The second
stage then estimates a strand-level hair reconstruction by reconciling in a
single optimization process the coarse volumetric constraints with hair strand
and hairstyle priors learned from the synthetic data. To further increase the
reconstruction fidelity, we incorporate image-based losses into the fitting
process using a new differentiable renderer. The combined system, named Neural
Haircut, achieves high realism and personalization of the reconstructed
hairstyles.

本文提出了一种名为神经剪发的方法，可以从单眼视频或多视角图像中恢复出具有高逼真度和个性化的发型，该方法有两个阶段并通过不同 iable renderer，通过隐式体积表示对粗糙的头发和胸围形状及发型方向，以及头发的先验进行精确的几何重建，从而解决了现有方案面临的挑战。