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.

翻译：利用图像或视频数据生成逼真的人体三维重建对于各种通信和娱乐应用至关重要。尽管现有方法在身体和面部区域取得了令人印象深刻的结果，但由于其高度的机械复杂性，逼真的头发建模仍然具有挑战性。本文提出了一种方法，能够在不受控光照条件下从单目视频或多视角图像中实现发丝级别的精确头发几何重建。我们的方法分为两个阶段：第一阶段使用隐式体积表示对粗头发、躯干形状以及头发方向进行联合重建；第二阶段通过将粗体积约束与从合成数据中学习的发丝和发型先验在单一优化过程中协调，来估计发丝级别的头发重建。为了进一步提高重建保真度，我们利用一种新的可微分渲染器将基于图像的损失纳入拟合过程。该组合系统名为Neural Haircut，实现了重建发型的高度真实感和个性化。