Point-Supervised Skeleton-Based Human Action Segmentation
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866918375626637312 |
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| author | Wang, Hongsong Shen, Yiqin Yan, Pengbo Gui, Jie |
| author_facet | Wang, Hongsong Shen, Yiqin Yan, Pengbo Gui, Jie |
| contents | Skeleton-based temporal action segmentation is a fundamental yet challenging task, playing a crucial role in enabling intelligent systems to perceive and respond to human activities. While fully-supervised methods achieve satisfactory performance, they require costly frame-level annotations and are sensitive to ambiguous action boundaries. To address these issues, we introduce a point-supervised framework for skeleton-based action segmentation, where only a single frame per action segment is labeled. We leverage multimodal skeleton data, including joint, bone, and motion information, encoded via a pretrained unified model to extract rich feature representations. To generate reliable pseudo-labels, we propose a novel prototype similarity method and integrate it with two existing methods: energy function and constrained K-Medoids clustering. Multimodal pseudo-label integration is proposed to enhance the reliability of the pseudo-label and guide the model training. We establish new benchmarks on PKU-MMD (X-Sub and X-View), MCFS-22, and MCFS-130, and implement baselines for point-supervised skeleton-based human action segmentation. Extensive experiments show that our method achieves competitive performance, even surpassing some fully-supervised methods while significantly reducing annotation effort. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_06201 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Point-Supervised Skeleton-Based Human Action Segmentation Wang, Hongsong Shen, Yiqin Yan, Pengbo Gui, Jie Computer Vision and Pattern Recognition Skeleton-based temporal action segmentation is a fundamental yet challenging task, playing a crucial role in enabling intelligent systems to perceive and respond to human activities. While fully-supervised methods achieve satisfactory performance, they require costly frame-level annotations and are sensitive to ambiguous action boundaries. To address these issues, we introduce a point-supervised framework for skeleton-based action segmentation, where only a single frame per action segment is labeled. We leverage multimodal skeleton data, including joint, bone, and motion information, encoded via a pretrained unified model to extract rich feature representations. To generate reliable pseudo-labels, we propose a novel prototype similarity method and integrate it with two existing methods: energy function and constrained K-Medoids clustering. Multimodal pseudo-label integration is proposed to enhance the reliability of the pseudo-label and guide the model training. We establish new benchmarks on PKU-MMD (X-Sub and X-View), MCFS-22, and MCFS-130, and implement baselines for point-supervised skeleton-based human action segmentation. Extensive experiments show that our method achieves competitive performance, even surpassing some fully-supervised methods while significantly reducing annotation effort. |
| title | Point-Supervised Skeleton-Based Human Action Segmentation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2603.06201 |