MSPoint-Gait: Multi-Scale Point cloud analysis for 3D gait recognition via cross-modal learning

Li, Xinzhu, Yang, Yi, Chen, Yikun, Yue, Guanghui, Zhou, Wei, Wang, Ruomei, Mao, Xudong, Zheng, Juepeng, Zhou, Fan, Qiu, Ziqi and Zhao, Baoquan 2025. MSPoint-Gait: Multi-Scale Point cloud analysis for 3D gait recognition via cross-modal learning. Presented at: 2025 IEEE International Conference on Multimedia and Expo (ICME), Nantes, France, 30 June - 4 July 2025. 2025 IEEE International Conference on Multimedia and Expo (ICME). 2025 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp. 1-6. 10.1109/icme59968.2025.11209453

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Abstract

Recent advances in LiDAR technology have enabled privacy-preserving gait recognition using 3D point cloud data. However, existing approaches struggle with the inherent challenges of point cloud processing and understanding such as spatial sparsity, irregular sampling, and complex temporal dynamics. In this paper, we present MSPoint-Gait, a novel framework that addresses these challenges through multi-scale analysis and cross-modal learning. At the core of our framework lies a Depth-Aware Attention Module (DAAM) that leverages rich 3D geometric information to generate attention-weighted depth representations, enabling fine-grained feature extraction from point cloud sequences. We further introduce a Multi-Scale Spatio-Temporal (MSST) network that hierarchically captures both local and global gait patterns through adaptive convolution kernels across multiple spatial and temporal scales. These components are unified through a novel cross-modal learning strategy that effectively bridges the semantic gap between raw point clouds and structured depth representations. The proposed frame-work achieves state-of-the-art performance on the challenging SUSTech1K dataset, with 91.9% Rank-1 and 98.0% Rank-5 accuracy, demonstrating significant improvements over existing methods across various walking conditions and viewpoints.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Schools > Computer Science & Informatics
Publisher:	IEEE
ISBN:	9798331594961
ISSN:	1945-7871
Last Modified:	14 Nov 2025 10:30
URI:	https://orca.cardiff.ac.uk/id/eprint/182415

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