MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation

• URL: http://arxiv.org/abs/2508.12948v1
• Date: Mon, 18 Aug 2025 14:24:04 GMT
• Title: MaskSem: Semantic-Guided Masking for Learning 3D Hybrid High-Order Motion Representation
• Authors: Wei Wei, Shaojie Zhang, Yonghao Dang, Jianqin Yin,
• Abstract summary: MaskSem is a semantic-guided masking method for learning 3D hybrid high-order motion representations.<n>We propose using hybrid high-order motion as the reconstruction target, enabling the model to learn multi-order motion patterns.<n> Experiments show that MaskSem, combined with a vanilla transformer, improves skeleton-based action recognition.
• Score: 14.527924445224302
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Human action recognition is a crucial task for intelligent robotics, particularly within the context of human-robot collaboration research. In self-supervised skeleton-based action recognition, the mask-based reconstruction paradigm learns the spatial structure and motion patterns of the skeleton by masking joints and reconstructing the target from unlabeled data. However, existing methods focus on a limited set of joints and low-order motion patterns, limiting the model's ability to understand complex motion patterns. To address this issue, we introduce MaskSem, a novel semantic-guided masking method for learning 3D hybrid high-order motion representations. This novel framework leverages Grad-CAM based on relative motion to guide the masking of joints, which can be represented as the most semantically rich temporal orgions. The semantic-guided masking process can encourage the model to explore more discriminative features. Furthermore, we propose using hybrid high-order motion as the reconstruction target, enabling the model to learn multi-order motion patterns. Specifically, low-order motion velocity and high-order motion acceleration are used together as the reconstruction target. This approach offers a more comprehensive description of the dynamic motion process, enhancing the model's understanding of motion patterns. Experiments on the NTU60, NTU120, and PKU-MMD datasets show that MaskSem, combined with a vanilla transformer, improves skeleton-based action recognition, making it more suitable for applications in human-robot interaction.

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