Explicifying Neural Implicit Fields for Efficient Dynamic Human Avatar
  Modeling via a Neural Explicit Surface
Ruiqi Zhang Jie Chen Qiang Wang
This paper proposes a technique for efficiently modeling dynamic humans by
explicifying the implicit neural fields via a Neural Explicit Surface (NES).
Implicit neural fields have advantages over traditional explicit
representations in modeling dynamic 3D content from sparse observations and
effectively representing complex geometries and appearances. Implicit neural
fields defined in 3D space, however, are expensive to render due to the need
for dense sampling during volumetric rendering. Moreover, their memory
efficiency can be further optimized when modeling sparse 3D space. To overcome
these issues, the paper proposes utilizing Neural Explicit Surface (NES) to
explicitly represent implicit neural fields, facilitating memory and
computational efficiency. To achieve this, the paper creates a fully
differentiable conversion between the implicit neural fields and the explicit
rendering interface of NES, leveraging the strengths of both implicit and
explicit approaches. This conversion enables effective training of the hybrid
representation using implicit methods and efficient rendering by integrating
the explicit rendering interface with a newly proposed rasterization-based
neural renderer that only incurs a texture color query once for the initial ray
interaction with the explicit surface, resulting in improved inference
efficiency. NES describes dynamic human geometries with pose-dependent neural
implicit surface deformation fields and their dynamic neural textures both in
2D space, which is a more memory-efficient alternative to traditional 3D
methods, reducing redundancy and computational load. The comprehensive
experiments show that NES performs similarly to previous 3D approaches, with
greatly improved rendering speed and reduced memory cost.