# Unsupervised 3D Human Pose Representation with Viewpoint and Pose Disentanglement

arXiv: https://arxiv.org/abs/2007.07053 · HTML: https://mlsanity.com/paper/2007.07053/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Jul 2020.

## Built on by (most cited first, 30 of 32)

- [3D Human Action Representation Learning via Cross-View Consistency Pursuit](https://mlsanity.com/paper/2104.14466/) (2021)
- [Contrastive Learning from Extremely Augmented Skeleton Sequences for Self-supervised Action Recognition](https://mlsanity.com/paper/2112.03590/) (2021)
- [Skeleton-Contrastive 3D Action Representation Learning](https://mlsanity.com/paper/2108.03656/) (2021)
- [Actionlet-Dependent Contrastive Learning for Unsupervised Skeleton-Based Action Recognition](https://mlsanity.com/paper/2303.10904/) (2023)
- [CMD: Self-supervised 3D Action Representation Learning with Cross-modal Mutual Distillation](https://mlsanity.com/paper/2208.12448/) (2022)
- [ElePose: Unsupervised 3D Human Pose Estimation by Predicting Camera Elevation and Learning Normalizing Flows on 2D Poses](https://mlsanity.com/paper/2112.07088/) (2021)
- [Global-local Motion Transformer for Unsupervised Skeleton-based Action Learning](https://mlsanity.com/paper/2207.06101/) (2022)
- [Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations](https://mlsanity.com/paper/2211.13466/) (2022)
- [Hierarchically Self-Supervised Transformer for Human Skeleton Representation Learning](https://mlsanity.com/paper/2207.09644/) (2022)
- [Hierarchical Contrast for Unsupervised Skeleton-based Action Representation Learning](https://mlsanity.com/paper/2212.02082/) (2022)
- [HaLP: Hallucinating Latent Positives for Skeleton-based Self-Supervised Learning of Actions](https://mlsanity.com/paper/2304.00387/) (2023)
- [ViewCLR: Learning Self-supervised Video Representation for Unseen Viewpoints](https://mlsanity.com/paper/2112.03905/) (2021)
- [Spatiotemporal Decouple-and-Squeeze Contrastive Learning for Semi-Supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2302.02316/) (2023)
- [Unsupervised Motion Representation Learning with Capsule Autoencoders](https://mlsanity.com/paper/2110.00529/) (2021)
- [MoCaNet: Motion Retargeting in-the-wild via Canonicalization Networks](https://mlsanity.com/paper/2112.10082/) (2021)
- [Heterogeneous Skeleton-Based Action Representation Learning](https://mlsanity.com/paper/2506.03481/) (2025)
- [SCD-Net: Spatiotemporal Clues Disentanglement Network for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2309.05834/) (2023)
- [Identity-free Artificial Emotional Intelligence via Micro-Gesture Understanding](https://mlsanity.com/paper/2405.13206/) (2024)
- [Focalized Contrastive View-invariant Learning for Self-supervised Skeleton-based Action Recognition](https://mlsanity.com/paper/2304.00858/) (2023)
- [Self-Supervised Skeleton-Based Action Representation Learning: A Benchmark and Beyond](https://mlsanity.com/paper/2406.02978/) (2024)
- [Lifting 2D Human Pose to 3D with Domain Adapted 3D Body Concept](https://mlsanity.com/paper/2111.11969/) (2021)
- [Collaborating Domain-shared and Target-specific Feature Clustering for Cross-domain 3D Action Recognition](https://mlsanity.com/paper/2207.09767/) (2022)
- [Spatio-Temporal Joint Density Driven Learning for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2505.23012/) (2025)
- [Towards Efficient General Feature Prediction in Masked Skeleton Modeling](https://mlsanity.com/paper/2509.03609/) (2025)
- [Cross-Stream Contrastive Learning for Self-Supervised Skeleton-Based Action Recognition](https://mlsanity.com/paper/2305.02324/) (2023)
- [Skeleton2vec: A Self-supervised Learning Framework with Contextualized Target Representations for Skeleton Sequence](https://mlsanity.com/paper/2401.00921/) (2024)
- [Attack-Augmentation Mixing-Contrastive Skeletal Representation Learning](https://mlsanity.com/paper/2304.04023/) (2023)
- [I2MD: 3D Action Representation Learning with Inter- and Intra-modal Mutual Distillation](https://mlsanity.com/paper/2310.15568/) (2023)
- [Cross-modal Manifold Cutmix for Self-supervised Video Representation Learning](https://mlsanity.com/paper/2112.03906/) (2021)
- [Mirror-Aware Neural Humans](https://mlsanity.com/paper/2309.04750/) (2023)

## Abstract (arXiv)

Learning a good 3D human pose representation is important for human pose related tasks, e.g. human 3D pose estimation and action recognition. Within all these problems, preserving the intrinsic pose information and adapting to view variations are two critical issues. In this work, we propose a novel Siamese denoising autoencoder to learn a 3D pose representation by disentangling the pose-dependent and view-dependent feature from the human skeleton data, in a fully unsupervised manner. These two disentangled features are utilized together as the representation of the 3D pose. To consider both the kinematic and geometric dependencies, a sequential bidirectional recursive network (SeBiReNet) is further proposed to model the human skeleton data. Extensive experiments demonstrate that the learned representation 1) preserves the intrinsic information of human pose, 2) shows good transferability across datasets and tasks. Notably, our approach achieves state-of-the-art performance on two inherently different tasks: pose denoising and unsupervised action recognition. Code and models are available at: urlhttps://github.com/NIEQiang001/unsupervised-human-pose.git