SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition

Changxin YE; Jingqi LI; Xuqian XUE; Xianye BEN; Hongming SHAN; Junping ZHANG

doi:10.1631/ENG.ITEE.2026.0053

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SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition

Regular Papers | Updated：2026-10-08

- SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition
- SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition
- ENGINEERING Information Technology & Electronic Engineering Vol. 27, Issue 9, (2026)
- Affiliations：
  
  College of Computer Science and Artificial Intelligence, Fudan University, Shanghai 200438, China
  School of Information Science and Engineering, Shandong University, Qingdao 266237, China
  Institute of Science and Technology for Brain-inspired Intelligence, Fudan University, Shanghai 200433, China
- Author bio：
  
  ✉Junping ZHANG, jpzhang@fudan.edu.cn
- Funds：
- DOI：10.1631/ENG.ITEE.2026.0053
  CLC： TP391.41
- Received：25 February 2026，
  
  Revised：2026-07-10，
  
  Published：01 September 2026
- Accepted：
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Changxin YE, Jingqi LI, Xuqian XUE, et al. SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(9): 260053-11.
DOI：

Changxin YE, Jingqi LI, Xuqian XUE, et al. SigmaGait: silhouette-guided motion-augmentation module for depth-based gait recognition[J]. ENGINEERING Information Technology & Electronic Engineering, 2026, 27(9): 260053-11. DOI： 10.1631/ENG.ITEE.2026.0053.

摘要

Abstract

Precise depth data derived from light detection and ranging (LiDAR) have been shown to be effective in improving gait recognition performance. Those depth data

however

are coarse and lack clear segmentation boundaries

which often results in increased ambiguity in interpreting precise human motion and body shape

potentially limiting the model’s ability to learn discriminative gait-related features. To address this challenge

we propose a novel silhouette-guided motion-augmentation module for depth-based gait recognition

namely

SigmaGait. SigmaGait leverages the clear boundaries and homogeneous foregrounds of silhouettes to enhance the model’s awareness of part-level motions and fine-grained appearance features. Furthermore

we explore a pseudo-depth generation approach that leverages accessible red–green–blue (RGB) data for scenarios where LiDAR sensors are unavailable. By employing off-the-shelf skinned multi-person linear (SMPL) models

we synthesize pseudo-depth maps from estimated human meshes

bridging the performance gap between camera and LiDAR-reliant gait recognition. We empirically evaluate our approach on real and synthetic datasets including cloth-changing benchmark for person re-identification and gait recognition (CCPG)

SUSTech1K

and FreeGait. Our model achieves state-of-the-art performance on real LiDAR data

and our pseudo-depth maps improve accuracy on 2D datasets despite their synthetic origin.

关键词

Keywords

references

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