  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.
