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.

翻译：人体三维虚拟人是元宇宙中的重要元素之一，其建模效果直接影响人们的视觉体验。然而人体具有复杂的拓扑结构和丰富的细节，构建令人满意的模型往往成本高昂、耗时费力。近年来研究提出了一种新的方法——隐式神经表示，这是一种连续表示方法，能够以任意分辨率描述任意拓扑结构的物体。研究者将隐式神经表示应用于人体三维虚拟人建模，获得了优于传统方法的效果。本文全面综述了隐式神经表示在人体建模中的应用。首先介绍了占有场、有符号距离函数和神经辐射场三种隐式表示方法，并对本文研究的文献进行了分类。随后分别对比分析了隐式建模方法在躯干、手部和头部中的应用。最后指出了当前工作的不足，并为研究者提供了可行性建议。