# Gait Recognition in the Wild with Dense 3D Representations and A Benchmark

arXiv: https://arxiv.org/abs/2204.02569 · HTML: https://mlsanity.com/paper/2204.02569/

## Key facts

- Cited by 114 papers in the map (data through 14 Oct 2026).
- Builds on 24 of its 63 references that are in the map.
- First posted on arXiv 6 Apr 2022.

## Builds on (most cited first, 24 of 24)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Deep High-Resolution Representation Learning for Visual Recognition](https://mlsanity.com/paper/1908.07919/) (2019)
- [Objects as Points](https://mlsanity.com/paper/1904.07850/) (2019)
- [OpenPose: Realtime Multi-Person 2D Pose Estimation using Part Affinity Fields](https://mlsanity.com/paper/1812.08008/) (2018)
- [End-to-end Recovery of Human Shape and Pose](https://mlsanity.com/paper/1712.06584/) (2017)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [VIBE: Video Inference for Human Body Pose and Shape Estimation](https://mlsanity.com/paper/1912.05656/) (2019)
- [Deep Learning for Person Re-identification: A Survey and Outlook](https://mlsanity.com/paper/2001.04193/) (2020)
- [NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding](https://mlsanity.com/paper/1905.04757/) (2019)
- [Monocular, One-stage, Regression of Multiple 3D People](https://mlsanity.com/paper/2008.12272/) (2020)
- [FastReID: A Pytorch Toolbox for General Instance Re-identification](https://mlsanity.com/paper/2006.02631/) (2020)
- [Putting People in their Place: Monocular Regression of 3D People in Depth](https://mlsanity.com/paper/2112.08274/) (2021)
- [GaitSet: Regarding Gait as a Set for Cross-View Gait Recognition](https://mlsanity.com/paper/1811.06186/) (2018)
- [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)
- [3D Human Pose Estimation = 2D Pose Estimation + Matching](https://mlsanity.com/paper/1612.06524/) (2016)
- [Stronger, Faster and More Explainable: A Graph Convolutional Baseline for Skeleton-based Action Recognition](https://mlsanity.com/paper/2010.09978/) (2020)
- [Gait Recognition via Disentangled Representation Learning](https://mlsanity.com/paper/1904.04925/) (2019)
- [Recent Advances in Monocular 2D and 3D Human Pose Estimation: A Deep Learning Perspective](https://mlsanity.com/paper/2104.11536/) (2021)
- [Deep Multi-View Enhancement Hashing for Image Retrieval](https://mlsanity.com/paper/2002.00169/) (2020)
- [Explainable Person Re-Identification with Attribute-guided Metric Distillation](https://mlsanity.com/paper/2103.01451/) (2021)
- [Depth image denoising using nuclear norm and learning graph model](https://mlsanity.com/paper/2008.03741/) (2020)
- [Unsupervised Domain Adaptation for 3D Keypoint Estimation via View Consistency](https://mlsanity.com/paper/1712.05765/) (2017)
- [AutoGPart: Intermediate Supervision Search for Generalizable 3D Part Segmentation](https://mlsanity.com/paper/2203.06558/) (2022)

## Built on by (most cited first, 30 of 114)

- [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)
- [BigGait: Learning Gait Representation You Want by Large Vision Models](https://mlsanity.com/paper/2402.19122/) (2024)
- [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)
- [KeyPoint Relative Position Encoding for Face Recognition](https://mlsanity.com/paper/2403.14852/) (2024)
- [DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition](https://mlsanity.com/paper/2303.14953/) (2023)
- [Parsing is All You Need for Accurate Gait Recognition in the Wild](https://mlsanity.com/paper/2308.16739/) (2023)
- [GaitGCI: Generative Counterfactual Intervention for Gait Recognition](https://mlsanity.com/paper/2306.03428/) (2023)
- [GaitGL: Learning Discriminative Global-Local Feature Representations for Gait Recognition](https://mlsanity.com/paper/2208.01380/) (2022)
- [Learning Gait Representation from Massive Unlabelled Walking Videos: A Benchmark](https://mlsanity.com/paper/2206.13964/) (2022)
- [Gait Recognition in the Wild with Multi-hop Temporal Switch](https://mlsanity.com/paper/2209.00355/) (2022)
- [BiggerGait: Unlocking Gait Recognition with Layer-wise Representations from Large Vision Models](https://mlsanity.com/paper/2505.18132/) (2025)
- [Hierarchical Spatio-Temporal Representation Learning for Gait Recognition](https://mlsanity.com/paper/2307.09856/) (2023)
- [Gait Recognition in Large-scale Free Environment via Single LiDAR](https://mlsanity.com/paper/2211.12371/) (2022)
- [QAGait: Revisit Gait Recognition from a Quality Perspective](https://mlsanity.com/paper/2401.13531/) (2024)
- [OpenGait: A Comprehensive Benchmark Study for Gait Recognition towards Better Practicality](https://mlsanity.com/paper/2405.09138/) (2024)
- [Causality-inspired Discriminative Feature Learning in Triple Domains for Gait Recognition](https://mlsanity.com/paper/2407.12519/) (2024)
- [On Denoising Walking Videos for Gait Recognition](https://mlsanity.com/paper/2505.18582/) (2025)
- [ShARc: Shape and Appearance Recognition for Person Identification In-the-wild](https://mlsanity.com/paper/2310.15946/) (2023)
- [Gait Recognition Using 3-D Human Body Shape Inference](https://mlsanity.com/paper/2212.09042/) (2022)
- [A Multi-Stage Adaptive Feature Fusion Neural Network for Multimodal Gait Recognition](https://mlsanity.com/paper/2312.14410/) (2023)
- [Open-Set Biometrics: Beyond Good Closed-Set Models](https://mlsanity.com/paper/2407.16133/) (2024)
- [Exploring More from Multiple Gait Modalities for Human Identification](https://mlsanity.com/paper/2412.11495/) (2024)
- [Gait Recognition in the Wild: A Large-scale Benchmark and NAS-based Baseline](https://mlsanity.com/paper/2205.02692/) (2022)
- [GaitPT: Skeletons Are All You Need For Gait Recognition](https://mlsanity.com/paper/2308.10623/) (2023)
- [PsyMo: A Dataset for Estimating Self-Reported Psychological Traits from Gait](https://mlsanity.com/paper/2308.10631/) (2023)
- [Cross-Modality Gait Recognition: Bridging LiDAR and Camera Modalities for Human Identification](https://mlsanity.com/paper/2404.04120/) (2024)

## Abstract (arXiv)

Existing studies for gait recognition are dominated by 2D representations like the silhouette or skeleton of the human body in constrained scenes. However, humans live and walk in the unconstrained 3D space, so projecting the 3D human body onto the 2D plane will discard a lot of crucial information like the viewpoint, shape, and dynamics for gait recognition. Therefore, this paper aims to explore dense 3D representations for gait recognition in the wild, which is a practical yet neglected problem. In particular, we propose a novel framework to explore the 3D Skinned Multi-Person Linear (SMPL) model of the human body for gait recognition, named SMPLGait. Our framework has two elaborately-designed branches of which one extracts appearance features from silhouettes, the other learns knowledge of 3D viewpoints and shapes from the 3D SMPL model. In addition, due to the lack of suitable datasets, we build the first large-scale 3D representation-based gait recognition dataset, named Gait3D. It contains 4,000 subjects and over 25,000 sequences extracted from 39 cameras in an unconstrained indoor scene. More importantly, it provides 3D SMPL models recovered from video frames which can provide dense 3D information of body shape, viewpoint, and dynamics. Based on Gait3D, we comprehensively compare our method with existing gait recognition approaches, which reflects the superior performance of our framework and the potential of 3D representations for gait recognition in the wild. The code and dataset are available at https://gait3d.github.io.