# Disentangled Human Body Embedding Based on Deep Hierarchical Neural Network

arXiv: https://arxiv.org/abs/1905.05622 · HTML: https://mlsanity.com/paper/1905.05622/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 May 2019.

## Built on by (most cited first, 21 of 21)

- [SNARF: Differentiable Forward Skinning for Animating Non-Rigid Neural Implicit Shapes](https://mlsanity.com/paper/2104.03953/) (2021)
- [TAVA: Template-free Animatable Volumetric Actors](https://mlsanity.com/paper/2206.08929/) (2022)
- [SelfRecon: Self Reconstruction Your Digital Avatar from Monocular Video](https://mlsanity.com/paper/2201.12792/) (2022)
- [Driving-Signal Aware Full-Body Avatars](https://mlsanity.com/paper/2105.10441/) (2021)
- [StereoPIFu: Depth Aware Clothed Human Digitization via Stereo Vision](https://mlsanity.com/paper/2104.05289/) (2021)
- [Multiway Non-rigid Point Cloud Registration via Learned Functional Map Synchronization](https://mlsanity.com/paper/2111.12878/) (2021)
- [Frame Averaging for Equivariant Shape Space Learning](https://mlsanity.com/paper/2112.01741/) (2021)
- [Neural-ABC: Neural Parametric Models for Articulated Body with Clothes](https://mlsanity.com/paper/2404.04673/) (2024)
- [ShapeFusion: A 3D diffusion model for localized shape editing](https://mlsanity.com/paper/2403.19773/) (2024)
- [A structured latent space for human body motion generation](https://mlsanity.com/paper/2106.04387/) (2021)
- [Neural Human Deformation Transfer](https://mlsanity.com/paper/2109.01588/) (2021)
- [LatentHuman: Shape-and-Pose Disentangled Latent Representation for Human Bodies](https://mlsanity.com/paper/2111.15113/) (2021)
- [Deep Deformation Detail Synthesis for Thin Shell Models](https://mlsanity.com/paper/2102.11541/) (2021)
- [DiffSurf: A Transformer-based Diffusion Model for Generating and Reconstructing 3D Surfaces in Pose](https://mlsanity.com/paper/2408.14860/) (2024)
- [JADE: Joint-aware Latent Diffusion for 3D Human Generative Modeling](https://mlsanity.com/paper/2412.20470/) (2024)
- [Disentangling 3D Attributes from a Single 2D Image: Human Pose, Shape and Garment](https://mlsanity.com/paper/2208.03167/) (2022)
- [PGAHum: Prior-Guided Geometry and Appearance Learning for High-Fidelity Animatable Human Reconstruction](https://mlsanity.com/paper/2404.13862/) (2024)
- [MeshMamba: State Space Models for Articulated 3D Mesh Generation and Reconstruction](https://mlsanity.com/paper/2507.15212/) (2025)
- [Localized Shape Modelling with Global Coherence: An Inverse Spectral Approach](https://mlsanity.com/paper/2108.02161/) (2021)
- [GiNGR: Generalized Iterative Non-Rigid Point Cloud and Surface Registration Using Gaussian Process Regression](https://mlsanity.com/paper/2203.09986/) (2022)
- [Disentangled Human Body Representation Based on Unsupervised Semantic-Aware Learning](https://mlsanity.com/paper/2505.19049/) (2025)

## Abstract (arXiv)

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.