KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition

• URL: http://arxiv.org/abs/2409.09444v1
• Date: Sat, 14 Sep 2024 14:11:45 GMT
• Title: KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition
• Authors: Zhaoyu Chen, Xing Li, Qian Huang, Qiang Geng, Tianjin Yang, Shihao Han,
• Abstract summary: We introduce D-Hyperpoint, a novel data type generated through a-Hyperpointding module. D-Hyperpoint encapsulates both regional-momentary motion and global-static posture, effectively summarizing the unit human action at each moment. We also present a D-Hyperpoint KANMixer module, which is applied to nested groupings of D-Hyperpoints to learn information discrimination.
• Score: 14.653930908806357
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud sequence-based 3D action recognition has achieved impressive performance and efficiency. However, existing point cloud sequence modeling methods cannot adequately balance the precision of limb micro-movements with the integrity of posture macro-structure, leading to the loss of crucial information cues in action inference. To overcome this limitation, we introduce D-Hyperpoint, a novel data type generated through a D-Hyperpoint Embedding module. D-Hyperpoint encapsulates both regional-momentary motion and global-static posture, effectively summarizing the unit human action at each moment. In addition, we present a D-Hyperpoint KANsMixer module, which is recursively applied to nested groupings of D-Hyperpoints to learn the action discrimination information and creatively integrates Kolmogorov-Arnold Networks (KAN) to enhance spatio-temporal interaction within D-Hyperpoints. Finally, we propose KAN-HyperpointNet, a spatio-temporal decoupled network architecture for 3D action recognition. Extensive experiments on two public datasets: MSR Action3D and NTU-RGB+D 60, demonstrate the state-of-the-art performance of our method.

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