Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions

Zhiyang Lu; Wen Jiang; Tianren Wu; Zhichao Wang; Changwang Zhang; Siqi Shen; Ming Cheng

cs.CVMar 15, 2026

Walking Further: Semantic-aware Multimodal Gait Recognition Under Long-Range Conditions

Authors: Zhiyang Lu, Wen Jiang, Tianren Wu, Zhichao Wang, Changwang Zhang, Siqi Shen, Ming Cheng

Organizations: Fujian Key Laboratory of Sensing and Computing for Smart Cities, Xiamen University · OPPO Research Institute

Abstract

Gait recognition is an emerging biometric technology that enables non-intrusive and hard-to-spoof human identification. However, most existing methods are confined to short-range, unimodal settings and fail to generalize to long-range and cross-distance scenarios under real-world conditions. To address this gap, we present LRGait, the first LiDAR-Camera multimodal benchmark designed for robust long-range gait recognition across diverse outdoor distances and environments. We further propose EMGaitNet, an end-to-end framework tailored for long-range multimodal gait recognition. To bridge the modality gap between RGB images and point clouds, we introduce a semantic-guided fusion pipeline. A CLIP-based Semantic Mining (SeMi) module first extracts human body-part-aware semantic cues, which are then employed to align 2D and 3D features via a Semantic-Guided Alignment (SGA) module within a unified embedding space. A Symmetric Cross-Attention Fusion (SCAF) module hierarchically integrates visual contours and 3D geometric features, and a Spatio-Temporal (ST) module captures global gait dynamics. Extensive experiments on various gait datasets validate the effectiveness of our method.

Figures & tables

Figure 1: Visualization of our proposed multimodal dataset LRGait at various and long-range distances.

Dataset	Sensor	Viewpoint	Distance
CASIA-B ( Yu et al. 2006 )	Camera	11	2m $\sim$ 4m
CASIA-C ( Tan et al. 2006 )	Camera	1	N/A
TUM-GAID ( Hofmann et al. 2014 )	RGB-D	1	3.6m
SZTAKI-LGA ( Benedek et al. 2016 )	LiDAR	1	N/A
OU-MVLP ( Takemura et al. 2018b )	Camera	14	8m
GREW ( Zhu et al. 2021 )	Camera	882	N/A

Table 1: Comparison with public datasets for gait recognition, where “LR” refers to the long-range distance exceeding 30m, “CD” denotes cross-distance retrieval, and “D&N” represents day and night during data collection.

Figure 2: Visualizations under daytime and nighttime.

Figure 3: Statistics of the proposed LRGait.

Figure 4: Illustration of the proposed framework.

Figure 5: Details of the proposed SGA module.

Figure 6: Details of the proposed SCAF module.

Methods	Modality	Probe Sequence (Rank- $1$ Accuracy %)								Overall
Methods	Modality	NM	BG	CL	CR	UB	UN	OC	NT	R- $1$	R- $5$
GaitSet ( Chao et al. 2019 )	sil	69.1	68.3	37.4	65.0	63.1	67.2	61.0	23.0	65.0	84.8
GaitBase ( Fan et al. 2023 )	sil	81.3	77.3	49.6	75.7	75.4	76.7	81.4	25.8	76.0	89.1
SimpleView ( Goyal et al. 2021 )	depth	72.3	68.8	57.2	63.3	49.2	79.7	62.5	66.5	64.8	85.8
LidarGait ( Shen et al. 2023 )	depth	91.8	88.6	74.6	89.0	67.5	80.9	94.5	90.4	86.8	96.1
PointTransformer ( Zhao et al. 2021 )	pc	53.2	48.1	32.0	43.2	39.1	47.9	41.8	47.1	44.4	76.7

Table 2: Comparison of cross-view Rank-

1

accuracy under various conditions on the SUSTech1K dataset. The best results are highlighted in bold , while the second-best entries are underlined .

Methods	Modality	Probe Sequence (Rank- $1$ Accuracy %)								Overall
Methods	Modality	D-20	D-30	D-40	D-50	N-20	N-30	N-40	N-50	R- $1$	R- $5$
GaitBase ( Fan et al. 2023 )	sil	67.9	53.9	48.5	33.8	41.6	33.4	19.8	13.2	46.8	77.8
LidarGait ( Shen et al. 2023 )	depth	26.1	14.6	13.2	10.8	18.5	14.6	10.6	9.6	15.7	51.3
LidarGait++ ( Shen et al. 2025 )	pc	32.1	24.4	18.8	12.6	23.1	13.2	11.7	11.2	20.9	59.0
HMRNet ( Han et al. 2024 )	pc+depth	58.7	53.1	50.3	44.8	22.5	19.3	11.8	8.6	41.1	71.7
MMGaitFormer ( Cui and Kang 2023 )	depth+sil	72.4	70.2	58.1	62.7	40.3	26.8	15.5	7.6	57.1	80.4

Table 3: Comparison of cross-distance cross-view Rank-

1

accuracy under various settings on the proposed LRGait dataset.

Methods	Modality	R- $1$	R- $5$	mAP
GaitSet ( Chao et al. 2019 )	sil	57.1	71.9	64.0
GaitBase ( Fan et al. 2023 )	sil	62.6	75.3	68.6
LidarGait ( Shen et al. 2023 )	depth	74.2	88.8	80.7
PointNet++ ( Qi et al. 2017 )	pc	59.3	81.2	69.3
LidarGait++ ( Shen et al. 2025 )	pc	82.0	93.6	87.2
HMRNet ( Han et al. 2024 )	pc+depth	80.8	93.6	86.5

Table 4: Comparisons with SOTA methods on FreeGait.

Figure 7: Comparison of t-SNE visualizations.

Baseline	SeMi	SGA	ST	Overall R- $1$	Overall R- $5$
✓				52.3	70.2
✓		✓		58.5	75.9
✓	✓	✓		64.2	80.7
✓	✓	✓	✓	68.9	85.8

Table 5: Ablation studies on the LRGait dataset.

Figure 9: Statistical overview of weather conditions and behavioral variations in LRGait. (a) Number of subjects captured under different weather scenarios. (b) Distribution of activity-related variations (e.g., backpacks, suitcases) across all frames in the dataset.

Figure 10: 10-50m pedestrian point cloud data.

Figure 11: Diversity and challenges of LRGait data.

Gait Recognition

Gait recognition identifies people from their walking patterns, using visual appearance and motion cues across cameras and other sensors.

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Apr 17, 2026J/K move · Enter open · S save

MMGait: Towards Multi-Modal Gait Recognition

Chenye Wang, Qingyuan Cai, Saihui Hou, Aoqi Li+1

School of Artificial Intelligence, Beijing Normal University

Cross-Modal Representation Learning Gait Analysis Gait Recognition Multimodal Sensor Fusion

Open paper AskPDF arXiv

Gait recognition has emerged as a powerful biometric technique for identifying individuals at a distance without requiring user cooperation. Most existing methods focus primarily on RGB-derived modalities, which fall short in real-world scenarios requiring multi-modal collaboration and cross-modal retrieval. To overcome these challenges, we present MMGait, a comprehensive multi-modal gait benchmark integrating data from five heterogeneous sensors, including an RGB camera, a depth camera, an infrared camera, a LiDAR scanner, and a 4D Radar system. MMGait contains twelve modalities and 334,060 sequences from 725 subjects, enabling systematic exploration across geometric, photometric, and motion domains. Based on MMGait, we conduct extensive evaluations on single-modal, cross-modal, and multi-modal paradigms to analyze modality robustness and complementarity. Furthermore, we introduce a new task, Omni Multi-Modal Gait Recognition, which aims to unify the above three gait recognition paradigms within a single model. We also propose a simple yet powerful baseline, OmniGait, which learns a shared embedding space across diverse modalities and achieves promising recognition performance. The MMGait benchmark, codebase, and pretrained checkpoints are publicly available at https://github.com/BNU-IVC/MMGait.

Abstract

Gait Recognition

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