KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition
<|reference_start|>KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action Recognition: 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.<|reference_end|>
@article{chen2024kan-hyperpointnet,
    title={KAN-HyperpointNet for Point Cloud Sequence-Based 3D Human Action
  Recognition},
    author={Zhaoyu Chen, Xing Li, Qian Huang, Qiang Geng, Tianjin Yang, Shihao Han},
    journal={arXiv preprint arXiv:2409.09444},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.09444},
    primaryClass={cs.CV}
}