# Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network

## TL;DR

This paper introduces a hierarchical neural network architecture for learning disentangled 3D human body shape and pose embeddings, enabling accurate reconstruction and flexible body generation.

## Contribution

It proposes a novel hierarchical reconstruction pipeline and a large dataset for improved disentangled embedding learning of 3D human bodies.

## Key findings

- Achieves superior reconstruction accuracy.
- Enables flexible 3D human body generation.
- Demonstrates effectiveness in various applications.

## Abstract

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.