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 article 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.

中文翻译：

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基于深度层次神经网络的解缠结人体嵌入


人体因不同的身份或姿势而呈现出不同的形状，但身体形状在结构上具有一定的相似性，因此可以嵌入到低维空间中。本文提出了一种类似自动编码器的网络架构，用于学习专门针对 3D 人体的解缠结形状和姿势嵌入。这是受到基于变形的潜在表示学习的最新进展的启发。为了提高重建精度，我们提出了一种用于解缠过程的分层重建管道，并构建了具有一致连接性的人体模型大数据集，用于神经网络的学习。我们学习的嵌入不仅可以实现卓越的重建精度，还可以通过插值、双线性插值和潜在空间采样为 3D 人体生成提供极大的灵活性。大量实验的结果证明了我们学习的 3D 人体嵌入在各种应用中的强大功能。