Capturing the 3D human body is one of the important tasks in computer vision
with a wide range of applications such as virtual reality and sports analysis.
However, conventional frame cameras are limited by their temporal resolution
and dynamic range, which imposes constraints in real-world application setups.
Event cameras have the advantages of high temporal resolution and high dynamic
range (HDR), but the development of event-based methods is necessary to handle
data with different characteristics. This paper proposes a novel event-based
method for 3D pose estimation and human mesh recovery. Prior work on
event-based human mesh recovery require frames (images) as well as event data.
The proposed method solely relies on events; it carves 3D voxels by moving the
event camera around a stationary body, reconstructs the human pose and mesh by
attenuated rays, and fit statistical body models, preserving high-frequency
details. The experimental results show that the proposed method outperforms
conventional frame-based methods in the estimation accuracy of both pose and
body mesh. We also demonstrate results in challenging situations where a
conventional camera has motion blur. This is the first to demonstrate
event-only human mesh recovery, and we hope that it is the first step toward
achieving robust and accurate 3D human body scanning from vision sensors.

本文提出了一种新的基于事件的方法，用于 3D 姿态估计和人体网格恢复，通过移动事件相机，通过衰减光线雕刻 3D 体素，重建人体姿态和网格，并拟合统计身体模型，实验结果表明该方法在姿态和身体网格估计精度方面优于传统基于帧的方法。

使用动态事件相机进行 3D 人体扫描

3D Human Scan With A Moving Event Camera

Human bodies exhibit various shapes for different identities or poses, but
the body shape has certain similarities in structure and thus can be embedded
in a low-dimensional space. This paper presents an autoencoder-like network
architecture to learn disentangled shape and pose embedding specifically for
the 3D human body. This is inspired by recent progress of deformation-based
latent representation learning. To improve the reconstruction accuracy, we
propose a hierarchical reconstruction pipeline for the disentangling process
and construct a large dataset of human body models with consistent connectivity
for the learning of the neural network. Our learned embedding can not only
achieve superior reconstruction accuracy but also provide great flexibility in
3D human body generation via interpolation, bilinear interpolation, and latent
space sampling. The results from extensive experiments demonstrate the
powerfulness of our learned 3D human body embedding in various applications.

本文提出了一种像自动编码器一样的网络架构，以学习解缠人体的形状和姿势嵌入，特别是针对三维人体。通过分层重构流程和一个大数据集的构建，并对神经网络进行学习，提高了重构准确性。通过广泛实验，我们的学习到的三维人体嵌入在各种应用中展现了强大的能力。

基于深度分层神经网络的解耦人体嵌入

Disentangled Human Body Embedding Based on Deep Hierarchical Neural  Network

In this paper, we are concerned with the 2D and 3D geometric shape generation
by prescribing a set of characteristic values of a specific geometric body. One
of the major motivations of our study is the 3D human body generation in
various applications. We develop a novel method that can generate the desired
body with customized characteristic values. The proposed method follows a
machine-learning flavour that generates the inferred geometric body with the
input characteristic parameters from a training dataset. One of the critical
ingredients and novelties of our method is the borrowing of inverse scattering
techniques in the theory of wave propagation to the body generation. This is
done by establishing a delicate one-to-one correspondence between a geometric
body and the far-field pattern of a source scattering problem governed by the
Helmholtz system. It in turn enables us to establish a one-to-one
correspondence between the geometric body space and the function space defined
by the far-field patterns. Hence, the far-field patterns can act as the shape
generators. The shape generation with prescribed characteristic parameters is
achieved by first manipulating the shape generators and then reconstructing the
corresponding geometric body from the obtained shape generator by a stable
multiple-frequency Fourier method. Our method is easy to implement and produces
more efficient and stable body generations. We provide both theoretical
analysis and extensive numerical experiments for the proposed method. The study
is the first attempt to introduce inverse scattering approaches in combination
with machine learning to the geometric body generation and it opens up many
opportunities for further developments.

本文介绍了一种利用机器学习和反散射技术生成具有预设特征值的几何体的新方法，其中利用 Helmholtz 系统以及由源散射问题的远场模式确定了几何体形状的生成器。此方法可生成高效和稳定的 3D 人体，并为进一步的研究提供了可能性。