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。