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.

本文全面回顾了隐式神经表达在人体建模中的应用，比较分析了隐式建模方法在身体、手和头部的应用，并指出了当前工作的不足之处和研究人员的可用建议。

使用隐式神经表示进行人类 3D-avatar 建模：简要调查

Human 3D Avatar Modeling with Implicit Neural Representation: A Brief  Survey

3D representation and reconstruction of human bodies have been studied for a
long time in computer vision. Traditional methods rely mostly on parametric
statistical linear models, limiting the space of possible bodies to linear
combinations. It is only recently that some approaches try to leverage neural
implicit representations for human body modeling, and while demonstrating
impressive results, they are either limited by representation capability or not
physically meaningful and controllable. In this work, we propose a novel neural
implicit representation for the human body, which is fully differentiable and
optimizable with disentangled shape and pose latent spaces. Contrary to prior
work, our representation is designed based on the kinematic model, which makes
the representation controllable for tasks like pose animation, while
simultaneously allowing the optimization of shape and pose for tasks like 3D
fitting and pose tracking. Our model can be trained and fine-tuned directly on
non-watertight raw data with well-designed losses. Experiments demonstrate the
improved 3D reconstruction performance over SoTA approaches and show the
applicability of our method to shape interpolation, model fitting, pose
tracking, and motion retargeting.

本文提出了一种基于运动学模型的神经隐式表示方法，可以同时处理人体的形状和姿态，实现了更好的三维重建效果和任务控制能力。

LatentHuman：人体形态和姿势分离的潜在表征

LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human  Bodies

Person re-identification via 3D skeletons is an emerging topic with great
potential in security-critical applications. Existing methods typically learn
body and motion features from the body-joint trajectory, whereas they lack a
systematic way to model body structure and underlying relations of body
components beyond the scale of body joints. In this paper, we for the first
time propose a Self-supervised Multi-scale Skeleton Graph Encoding (SM-SGE)
framework that comprehensively models human body, component relations, and
skeleton dynamics from unlabeled skeleton graphs of various scales to learn an
effective skeleton representation for person Re-ID. Specifically, we first
devise multi-scale skeleton graphs with coarse-to-fine human body partitions,
which enables us to model body structure and skeleton dynamics at multiple
levels. Second, to mine inherent correlations between body components in
skeletal motion, we propose a multi-scale graph relation network to learn
structural relations between adjacent body-component nodes and collaborative
relations among nodes of different scales, so as to capture more discriminative
skeleton graph features. Last, we propose a novel multi-scale skeleton
reconstruction mechanism to enable our framework to encode skeleton dynamics
and high-level semantics from unlabeled skeleton graphs, which encourages
learning a discriminative skeleton representation for person Re-ID. Extensive
experiments show that SM-SGE outperforms most state-of-the-art skeleton-based
methods. We further demonstrate its effectiveness on 3D skeleton data estimated
from large-scale RGB videos. Our codes are open at
this https URL

本研究提出了一种自监督的多尺度骨架图编码框架，通过学习不同尺度的未标记骨架图来建模人体构造、骨架动态和部件关系以实现人体再识别，实验证明其性能优于现有骨架方法。

SM-SGE：自监督的多尺度骨架图编码框架用于人员再识别

SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework  for Person Re-Identification

Recovering the 3D shape of a person from its 2D appearance is ill-posed due
to ambiguities. Nevertheless, with the help of convolutional neural networks
(CNN) and prior knowledge on the 3D human body, it is possible to overcome such
ambiguities to recover detailed 3D shapes of human bodies from single images.
Current solutions, however, fail to reconstruct all the details of a person
wearing loose clothes. This is because of either (a) huge memory requirement
that cannot be maintained even on modern GPUs or (b) the compact 3D
representation that cannot encode all the details. In this paper, we propose
the tetrahedral outer shell volumetric truncated signed distance function
(TetraTSDF) model for the human body, and its corresponding part connection
network (PCN) for 3D human body shape regression. Our proposed model is
compact, dense, accurate, and yet well suited for CNN-based regression task.
Our proposed PCN allows us to learn the distribution of the TSDF in the
tetrahedral volume from a single image in an end-to-end manner. Results show
that our proposed method allows to reconstruct detailed shapes of humans
wearing loose clothes from single RGB images.

该研究提出了一种基于 TetraTSDF 和 PCN 的人体建模方法，通过卷积神经网络和三维人体先验知识，可以从单张图像中重建穿着宽松衣服的真实 3D 人体模型。