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| Format: | Preprint |
| Veröffentlicht: |
2025
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| Online-Zugang: | https://arxiv.org/abs/2511.05611 |
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| _version_ | 1866915606030188544 |
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| author | Zhu, Shuaikang Yang, Yang Sun, Chen |
| author_facet | Zhu, Shuaikang Yang, Yang Sun, Chen |
| contents | Human pose serves as a cornerstone of action quality assessment (AQA), where subtle spatial-temporal variations in pose often distinguish excellence from mediocrity. In high-level competitions, these nuanced differences become decisive factors in scoring. In this paper, we propose a novel multi-level motion parsing framework for AQA based on enhanced spatial-temporal pose features. On the first level, the Action-Unit Parser is designed with the help of pose extraction to achieve precise action segmentation and comprehensive local-global pose representations. On the second level, Motion Parser is used by spatial-temporal feature learning to capture pose changes and appearance details for each action-unit. Meanwhile, some special conditions other than body-related will impact action scoring, like water splash in diving. In this work, we design an additional Condition Parser to offer users more flexibility in their choices. Finally, Weight-Adjust Scoring Module is introduced to better accommodate the diverse requirements of various action types and the multi-scale nature of action-units. Extensive evaluations on large-scale diving sports datasets demonstrate that our multi-level motion parsing framework achieves state-of-the-art performance in both action segmentation and action scoring tasks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_05611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Pose-Aware Multi-Level Motion Parsing for Action Quality Assessment Zhu, Shuaikang Yang, Yang Sun, Chen Computer Vision and Pattern Recognition Human pose serves as a cornerstone of action quality assessment (AQA), where subtle spatial-temporal variations in pose often distinguish excellence from mediocrity. In high-level competitions, these nuanced differences become decisive factors in scoring. In this paper, we propose a novel multi-level motion parsing framework for AQA based on enhanced spatial-temporal pose features. On the first level, the Action-Unit Parser is designed with the help of pose extraction to achieve precise action segmentation and comprehensive local-global pose representations. On the second level, Motion Parser is used by spatial-temporal feature learning to capture pose changes and appearance details for each action-unit. Meanwhile, some special conditions other than body-related will impact action scoring, like water splash in diving. In this work, we design an additional Condition Parser to offer users more flexibility in their choices. Finally, Weight-Adjust Scoring Module is introduced to better accommodate the diverse requirements of various action types and the multi-scale nature of action-units. Extensive evaluations on large-scale diving sports datasets demonstrate that our multi-level motion parsing framework achieves state-of-the-art performance in both action segmentation and action scoring tasks. |
| title | Pose-Aware Multi-Level Motion Parsing for Action Quality Assessment |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.05611 |