# Space-Time Representation of People Based on 3D Skeletal Data: A Review

arXiv: https://arxiv.org/abs/1601.01006 · HTML: https://mlsanity.com/paper/1601.01006/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Jan 2016.

## Built on by (most cited first, 24 of 24)

- [ProtoRes: Proto-Residual Network for Pose Authoring via Learned Inverse Kinematics](https://mlsanity.com/paper/2106.01981/) (2021)
- [Action2video: Generating Videos of Human 3D Actions](https://mlsanity.com/paper/2111.06925/) (2021)
- [PSUMNet: Unified Modality Part Streams are All You Need for Efficient Pose-based Action Recognition](https://mlsanity.com/paper/2208.05775/) (2022)
- [TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification](https://mlsanity.com/paper/2303.06819/) (2023)
- [Humans as Light Bulbs: 3D Human Reconstruction from Thermal Reflection](https://mlsanity.com/paper/2305.01652/) (2023)
- [Hierarchical Skeleton Meta-Prototype Contrastive Learning with Hard Skeleton Mining for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2307.12917/) (2023)
- [SM-SGE: A Self-Supervised Multi-Scale Skeleton Graph Encoding Framework for Person Re-Identification](https://mlsanity.com/paper/2107.01903/) (2021)
- [Continual Spatio-Temporal Graph Convolutional Networks](https://mlsanity.com/paper/2203.11009/) (2022)
- [Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification](https://mlsanity.com/paper/2106.03069/) (2021)
- [From CNNs to Transformers in Multimodal Human Action Recognition: A Survey](https://mlsanity.com/paper/2405.15813/) (2024)
- [SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2204.09826/) (2022)
- [Learning from Temporal Spatial Cubism for Cross-Dataset Skeleton-based Action Recognition](https://mlsanity.com/paper/2207.08095/) (2022)
- [Multi-Scale Semantics-Guided Neural Networks for Efficient Skeleton-Based Human Action Recognition](https://mlsanity.com/paper/2111.03993/) (2021)
- [Skeleton Prototype Contrastive Learning with Multi-Level Graph Relation Modeling for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2208.11814/) (2022)
- [Motion Diffusion Autoencoders: Enabling Attribute Manipulation in Human Motion Demonstrated on Karate Techniques](https://mlsanity.com/paper/2501.18729/) (2025)
- [Motif Guided Graph Transformer with Combinatorial Skeleton Prototype Learning for Skeleton-Based Person Re-Identification](https://mlsanity.com/paper/2412.09044/) (2024)
- [Real-Time Online Skeleton Extraction and Gesture Recognition on Pepper](https://mlsanity.com/paper/2206.11376/) (2022)
- [A Survey on 3D Skeleton Based Person Re-Identification: Taxonomy, Advances, Challenges, and Interdisciplinary Prospects](https://mlsanity.com/paper/2401.15296/) (2024)
- [Active Generation Network of Human Skeleton for Action Recognition](https://mlsanity.com/paper/2401.17086/) (2024)
- [Evaluation Framework for Feedback Generation Methods in Skeletal Movement Assessment](https://mlsanity.com/paper/2404.09359/) (2024)
- [ProGraph: Temporally-alignable Probability Guided Graph Topological Modeling for 3D Human Reconstruction](https://mlsanity.com/paper/2411.04399/) (2024)
- [Monkey Transfer Learning Can Improve Human Pose Estimation](https://mlsanity.com/paper/2412.15966/) (2024)
- [Towards Skeletal and Signer Noise Reduction in Sign Language Production via Quaternion-Based Pose Encoding and Contrastive Learning](https://mlsanity.com/paper/2508.14574/) (2025)
- [A Point Cloud Transformer for Remote Monitoring and Automated Assessment of Physical Rehabilitation Exercises](https://mlsanity.com/paper/2606.30309/) (2026)

## Abstract (arXiv)

Spatiotemporal human representation based on 3D visual perception data is a rapidly growing research area. Based on the information sources, these representations can be broadly categorized into two groups based on RGB-D information or 3D skeleton data. Recently, skeleton-based human representations have been intensively studied and kept attracting an increasing attention, due to their robustness to variations of viewpoint, human body scale and motion speed as well as the realtime, online performance. This paper presents a comprehensive survey of existing space-time representations of people based on 3D skeletal data, and provides an informative categorization and analysis of these methods from the perspectives, including information modality, representation encoding, structure and transition, and feature engineering. We also provide a brief overview of skeleton acquisition devices and construction methods, enlist a number of public benchmark datasets with skeleton data, and discuss potential future research directions.