Human 3D Avatar Modeling with Implicit Neural Representation: A Brief
  Survey
Mingyang Sun Dingkang Yang Dongliang Kou Yang Jiang Weihua Shan Zhe Yan Lihua Zhang
A human 3D avatar is one of the important elements in the metaverse, and the
modeling effect directly affects people's visual experience. However, the human
body has a complex topology and diverse details, so it is often expensive,
time-consuming, and laborious to build a satisfactory model. Recent studies
have proposed a novel method, implicit neural representation, which is a
continuous representation method and can describe objects with arbitrary
topology at arbitrary resolution. Researchers have applied implicit neural
representation to human 3D avatar modeling and obtained more excellent results
than traditional methods. This paper comprehensively reviews the application of
implicit neural representation in human body modeling. First, we introduce
three implicit representations of occupancy field, SDF, and NeRF, and make a
classification of the literature investigated in this paper. Then the
application of implicit modeling methods in the body, hand, and head are
compared and analyzed respectively. Finally, we point out the shortcomings of
current work and provide available suggestions for researchers.