3D Skeleton-Based Action Recognition: A Review
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866910979813539840 |
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| author | Liu, Mengyuan Liu, Hong Hu, Qianshuo Ren, Bin Yuan, Junsong Lin, Jiaying Wen, Jiajun |
| author_facet | Liu, Mengyuan Liu, Hong Hu, Qianshuo Ren, Bin Yuan, Junsong Lin, Jiaying Wen, Jiajun |
| contents | With the inherent advantages of skeleton representation, 3D skeleton-based action recognition has become a prominent topic in the field of computer vision. However, previous reviews have predominantly adopted a model-oriented perspective, often neglecting the fundamental steps involved in skeleton-based action recognition. This oversight tends to ignore key components of skeleton-based action recognition beyond model design and has hindered deeper, more intrinsic understanding of the task. To bridge this gap, our review aims to address these limitations by presenting a comprehensive, task-oriented framework for understanding skeleton-based action recognition. We begin by decomposing the task into a series of sub-tasks, placing particular emphasis on preprocessing steps such as modality derivation and data augmentation. The subsequent discussion delves into critical sub-tasks, including feature extraction and spatio-temporal modeling techniques. Beyond foundational action recognition networks, recently advanced frameworks such as hybrid architectures, Mamba models, large language models (LLMs), and generative models have also been highlighted. Finally, a comprehensive overview of public 3D skeleton datasets is presented, accompanied by an analysis of state-of-the-art algorithms evaluated on these benchmarks. By integrating task-oriented discussions, comprehensive examinations of sub-tasks, and an emphasis on the latest advancements, our review provides a fundamental and accessible structured roadmap for understanding and advancing the field of 3D skeleton-based action recognition. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_00915 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | 3D Skeleton-Based Action Recognition: A Review Liu, Mengyuan Liu, Hong Hu, Qianshuo Ren, Bin Yuan, Junsong Lin, Jiaying Wen, Jiajun Computer Vision and Pattern Recognition With the inherent advantages of skeleton representation, 3D skeleton-based action recognition has become a prominent topic in the field of computer vision. However, previous reviews have predominantly adopted a model-oriented perspective, often neglecting the fundamental steps involved in skeleton-based action recognition. This oversight tends to ignore key components of skeleton-based action recognition beyond model design and has hindered deeper, more intrinsic understanding of the task. To bridge this gap, our review aims to address these limitations by presenting a comprehensive, task-oriented framework for understanding skeleton-based action recognition. We begin by decomposing the task into a series of sub-tasks, placing particular emphasis on preprocessing steps such as modality derivation and data augmentation. The subsequent discussion delves into critical sub-tasks, including feature extraction and spatio-temporal modeling techniques. Beyond foundational action recognition networks, recently advanced frameworks such as hybrid architectures, Mamba models, large language models (LLMs), and generative models have also been highlighted. Finally, a comprehensive overview of public 3D skeleton datasets is presented, accompanied by an analysis of state-of-the-art algorithms evaluated on these benchmarks. By integrating task-oriented discussions, comprehensive examinations of sub-tasks, and an emphasis on the latest advancements, our review provides a fundamental and accessible structured roadmap for understanding and advancing the field of 3D skeleton-based action recognition. |
| title | 3D Skeleton-Based Action Recognition: A Review |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.00915 |