Neural reconstruction and rendering strategies have demonstrated state-of-the-art performances due, in part, to their ability to preserve high level shape details. Existing approaches, however, either represent objects as implicit surface functions or neural volumes and still struggle to recover shapes with heterogeneous materials, in particular human skin, hair or clothes. To this aim, we present a new hybrid implicit surface representation to model human shapes. This representation is composed of two surface layers that represent opaque and translucent regions on the clothed human body. We segment different regions automatically using visual cues and learn to reconstruct two signed distance functions (SDFs). We perform surface-based rendering on opaque regions (e.g., body, face, clothes) to preserve high-fidelity surface normals and volume rendering on translucent regions (e.g., hair). Experiments demonstrate that our approach obtains state-of-the-art results on 3D human reconstructions, and also shows competitive performances on other objects.

翻译：神经重建与渲染策略因能保留高级形状细节而展现出最优性能。然而，现有方法或将物体表示为隐式表面函数，或表示为神经体积，仍需艰难恢复具有异质材质（尤其是人类皮肤、毛发或衣物）的形状。为此，我们提出一种新的混合隐式表面表示来建模人体形状。该表示由两个表面层构成，分别代表穿衣人体上的不透明区域和半透明区域。我们利用视觉线索自动分割不同区域，并学习重建两个符号距离函数。对不透明区域（如身体、面部、衣物）执行基于表面的渲染以保留高保真表面法线，对半透明区域（如毛发）执行体积渲染。实验表明，我们的方法在三维人体重建上取得了最优结果，并在其他物体上展现出竞争性表现。