STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition

• URL: http://arxiv.org/abs/2303.18177v2
• Date: Fri, 26 Jul 2024 19:13:29 GMT
• Title: STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition
• Authors: Xiaoyu Zhu, Po-Yao Huang, Junwei Liang, Celso M. de Melo, Alexander Hauptmann,
• Abstract summary: We study the problem of human action recognition using motion capture (MoCap) sequences. We propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models.
• Score: 50.064502884594376
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standardized skeleton representations as model input, we propose a novel Spatial-Temporal Mesh Transformer (STMT) to directly model the mesh sequences. The model uses a hierarchical transformer with intra-frame off-set attention and inter-frame self-attention. The attention mechanism allows the model to freely attend between any two vertex patches to learn non-local relationships in the spatial-temporal domain. Masked vertex modeling and future frame prediction are used as two self-supervised tasks to fully activate the bi-directional and auto-regressive attention in our hierarchical transformer. The proposed method achieves state-of-the-art performance compared to skeleton-based and point-cloud-based models on common MoCap benchmarks. Code is available at https://github.com/zgzxy001/STMT.

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