# SkeletonGait: Gait Recognition Using Skeleton Maps

arXiv: https://arxiv.org/abs/2311.13444 · HTML: https://mlsanity.com/paper/2311.13444/

## Key facts

- Cited by 41 papers in the map (data through 14 Oct 2026).
- Builds on 10 of its 21 references that are in the map.
- First posted on arXiv 22 Nov 2023.

## Builds on (most cited first, 10 of 10)

- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Gait Recognition via Effective Global-Local Feature Representation and Local Temporal Aggregation](https://mlsanity.com/paper/2011.01461/) (2020)
- [GaitGraph: Graph Convolutional Network for Skeleton-Based Gait Recognition](https://mlsanity.com/paper/2101.11228/) (2021)
- [Gait Recognition in the Wild with Dense 3D Representations and A Benchmark](https://mlsanity.com/paper/2204.02569/) (2022)
- [OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://mlsanity.com/paper/2211.06597/) (2022)
- [LidarGait: Benchmarking 3D Gait Recognition with Point Clouds](https://mlsanity.com/paper/2211.10598/) (2022)
- [Exploring Deep Models for Practical Gait Recognition](https://mlsanity.com/paper/2303.03301/) (2023)
- [A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://mlsanity.com/paper/2206.13732/) (2022)
- [Learning Rich Features for Gait Recognition by Integrating Skeletons and Silhouettes](https://mlsanity.com/paper/2110.13408/) (2021)

## Built on by (most cited first, 30 of 41)

- [BigGait: Learning Gait Representation You Want by Large Vision Models](https://mlsanity.com/paper/2402.19122/) (2024)
- [BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models](https://mlsanity.com/paper/2505.18132/) (2025)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [Person Recognition at Altitude and Range: Fusion of Face, Body Shape and Gait](https://mlsanity.com/paper/2505.04616/) (2025)
- [On Denoising Walking Videos for Gait Recognition](https://mlsanity.com/paper/2505.18582/) (2025)
- [It Takes Two: Accurate Gait Recognition in the Wild via Cross-granularity Alignment](https://mlsanity.com/paper/2411.10742/) (2024)
- [GaitContour: Efficient Gait Recognition based on a Contour-Pose Representation](https://mlsanity.com/paper/2311.16497/) (2023)
- [DAGait: Generalized Skeleton-Guided Data Alignment for Gait Recognition](https://mlsanity.com/paper/2503.18830/) (2025)
- [GaitSnippet: Gait Recognition Beyond Unordered Sets and Ordered Sequences](https://mlsanity.com/paper/2508.07782/) (2025)
- [CarGait: Cross-Attention based Re-ranking for Gait recognition](https://mlsanity.com/paper/2503.03501/) (2025)
- [On Model and Data Scaling for Skeleton-based Self-Supervised Gait Recognition](https://mlsanity.com/paper/2504.07598/) (2025)
- [TrackletGait: A Robust Framework for Gait Recognition in the Wild](https://mlsanity.com/paper/2508.02143/) (2025)
- [Pose as Clinical Prior: Learning Dual Representations for Scoliosis Screening](https://mlsanity.com/paper/2509.00872/) (2025)
- [Silhouette-based Gait Foundation Model](https://mlsanity.com/paper/2512.00691/) (2025)
- [EventGait: Towards Robust Gait Recognition with Event Streams](https://mlsanity.com/paper/2605.22139/) (2026)
- [SpheriGait: Enriching Spatial Representation via Spherical Projection for LiDAR-based Gait Recognition](https://mlsanity.com/paper/2409.11869/) (2024)
- [MimicGait: A Model Agnostic approach for Occluded Gait Recognition using Correlational Knowledge Distillation](https://mlsanity.com/paper/2501.15666/) (2025)
- [Mesh-Gait: A Unified Framework for Gait Recognition Through Multi-Modal Representation Learning from 2D Silhouettes](https://mlsanity.com/paper/2510.10406/) (2025)
- [Combo-Gait: Unified Transformer Framework for Multi-Modal Gait Recognition and Attribute Analysis](https://mlsanity.com/paper/2510.10417/) (2025)
- [Gait Recognition via Collaborating Discriminative and Generative Diffusion Models](https://mlsanity.com/paper/2511.06245/) (2025)
- [Is Visual Realism Enough? Evaluating Gait Biometric Fidelity in Generative AI Human Animation](https://mlsanity.com/paper/2512.19275/) (2025)
- [Beyond Motion Pattern: An Empirical Study of Physical Forces for Human Motion Understanding](https://mlsanity.com/paper/2512.20451/) (2025)
- [GaitKD: A Universal Decoupled Distillation Framework for Efficient Gait Recognition](https://mlsanity.com/paper/2604.26255/) (2026)
- [Text-guided Feature Disentanglement for Cross-modal Gait Recognition](https://mlsanity.com/paper/2605.30784/) (2026)
- [Decoding Children's Gait Behavior](https://mlsanity.com/paper/2608.00371/) (2026)
- [SapiensID 2.0: Aligning Human Recognition Foundation Models with Human Perception](https://mlsanity.com/paper/2608.10497/) (2026)
- [Vocabulary-Guided Gait Recognition](https://mlsanity.com/paper/2609.18413/) (2026)
- [A Survey on 3D Skeleton Based Person Re-Identification: Taxonomy, Advances, Challenges, and Interdisciplinary Prospects](https://mlsanity.com/paper/2401.15296/) (2024)
- [HorGait: A Hybrid Model for Accurate Gait Recognition in LiDAR Point Cloud Planar Projections](https://mlsanity.com/paper/2410.08454/) (2024)
- [Database-Agnostic Gait Enrollment using SetTransformers](https://mlsanity.com/paper/2505.02815/) (2025)

## Abstract (arXiv)

The choice of the representations is essential for deep gait recognition methods. The binary silhouettes and skeletal coordinates are two dominant representations in recent literature, achieving remarkable advances in many scenarios. However, inherent challenges remain, in which silhouettes are not always guaranteed in unconstrained scenes, and structural cues have not been fully utilized from skeletons. In this paper, we introduce a novel skeletal gait representation named skeleton map, together with SkeletonGait, a skeleton-based method to exploit structural information from human skeleton maps. Specifically, the skeleton map represents the coordinates of human joints as a heatmap with Gaussian approximation, exhibiting a silhouette-like image devoid of exact body structure. Beyond achieving state-of-the-art performances over five popular gait datasets, more importantly, SkeletonGait uncovers novel insights about how important structural features are in describing gait and when they play a role. Furthermore, we propose a multi-branch architecture, named SkeletonGait++, to make use of complementary features from both skeletons and silhouettes. Experiments indicate that SkeletonGait++ outperforms existing state-of-the-art methods by a significant margin in various scenarios. For instance, it achieves an impressive rank-1 accuracy of over 85% on the challenging GREW dataset. All the source code is available at https://github.com/ShiqiYu/OpenGait.