# A Survey on 3D Skeleton Based Person Re-Identification: Taxonomy, Advances, Challenges, and Interdisciplinary Prospects

arXiv: https://arxiv.org/abs/2401.15296 · HTML: https://mlsanity.com/paper/2401.15296/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 27 of its 67 references that are in the map.
- First posted on arXiv 27 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [Masked Autoencoders Are Scalable Vision Learners](https://mlsanity.com/paper/2111.06377/) (2021)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition](https://mlsanity.com/paper/1801.07455/) (2018)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [A Survey on Knowledge Graphs: Representation, Acquisition and Applications](https://mlsanity.com/paper/2002.00388/) (2020)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [Person Transfer GAN to Bridge Domain Gap for Person Re-Identification](https://mlsanity.com/paper/1711.08565/) (2017)
- [Channel-wise Topology Refinement Graph Convolution for Skeleton-Based Action Recognition](https://mlsanity.com/paper/2107.12213/) (2021)
- [Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Prediction](https://mlsanity.com/paper/2003.08802/) (2020)
- [3D Human Pose Estimation = 2D Pose Estimation + Matching](https://mlsanity.com/paper/1612.06524/) (2016)
- [PoseGPT: Quantization-based 3D Human Motion Generation and Forecasting](https://mlsanity.com/paper/2210.10542/) (2022)
- [SkeletonGait: Gait Recognition Using Skeleton Maps](https://mlsanity.com/paper/2311.13444/) (2023)
- [GPGait: Generalized Pose-based Gait Recognition](https://mlsanity.com/paper/2303.05234/) (2023)
- [Spatial Transformer Network on Skeleton-based Gait Recognition](https://mlsanity.com/paper/2204.03873/) (2022)
- [Space-Time Representation of People Based on 3D Skeletal Data: A Review](https://mlsanity.com/paper/1601.01006/) (2016)
- [A Self-Supervised Gait Encoding Approach with Locality-Awareness for 3D Skeleton Based Person Re-Identification](https://mlsanity.com/paper/2009.03671/) (2020)
- [Recurrent Attention Models for Depth-Based Person Identification](https://mlsanity.com/paper/1611.07212/) (2016)
- [Skeleton-aided Articulated Motion Generation](https://mlsanity.com/paper/1707.01058/) (2017)
- [TranSG: Transformer-Based Skeleton Graph Prototype Contrastive Learning with Structure-Trajectory Prompted Reconstruction for Person Re-Identification](https://mlsanity.com/paper/2303.06819/) (2023)
- [Brain-inspired Search Engine Assistant based on Knowledge Graph](https://mlsanity.com/paper/2012.13529/) (2020)
- [Self-Supervised Gait Encoding with Locality-Aware Attention for Person Re-Identification](https://mlsanity.com/paper/2008.09435/) (2020)
- [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)
- [Keypoint Message Passing for Video-based Person Re-Identification](https://mlsanity.com/paper/2111.08279/) (2021)
- [Multi-Level Graph Encoding with Structural-Collaborative Relation Learning for Skeleton-Based Person Re-Identification](https://mlsanity.com/paper/2106.03069/) (2021)
- [SimMC: Simple Masked Contrastive Learning of Skeleton Representations for Unsupervised Person Re-Identification](https://mlsanity.com/paper/2204.09826/) (2022)

## Abstract (arXiv)

Person re-identification via 3D skeletons is an important emerging research area that attracts increasing attention within the pattern recognition community. With distinctive advantages across various application scenarios, numerous 3D skeleton based person re-identification (SRID) methods with diverse skeleton modeling and learning paradigms have been proposed in recent years. In this paper, we provide a comprehensive review and analysis of recent SRID advances. First of all, we define the SRID task and provide an overview of its origin and major advancements. Secondly, we formulate a systematic taxonomy that organizes existing methods into three categories centered on hand-crafted, sequence-based, and graph-based modeling. Then, we elaborate on the representative models along these three types with an illustration of foundational mechanisms. Meanwhile, we provide an overview of mainstream supervised, self-supervised, and unsupervised SRID learning paradigms and corresponding common methods. A thorough evaluation of state-of-the-art SRID methods is further conducted over various types of benchmarks and protocols to compare their effectiveness, efficiency, and key properties. Finally, we present the key challenges and prospects to advance future research, and highlight interdisciplinary applications of SRID with a case study.