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• W4386075706 abstract "We study the problem of human action recognition using motion capture (MoCap) sequences. Unlike existing techniques that take multiple manual steps to derive standard-ized 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 nonlocal 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." @default.
• W4386075706 created "2023-08-23" @default.
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• W4386075706 date "2023-06-01" @default.
• W4386075706 modified "2023-10-12" @default.
• W4386075706 title "STMT: A Spatial-Temporal Mesh Transformer for MoCap-Based Action Recognition" @default.
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• W4386075706 doi "https://doi.org/10.1109/cvpr52729.2023.00153" @default.
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