Pose-Aware Multi-Level Motion Parsing for Action Quality Assessment

• URL: http://arxiv.org/abs/2511.05611v1
• Date: Thu, 06 Nov 2025 13:00:22 GMT
• Title: Pose-Aware Multi-Level Motion Parsing for Action Quality Assessment
• Authors: Shuaikang Zhu, Yang Yang, Chen Sun,
• Abstract summary: Human pose serves as a cornerstone of action quality assessment (AQA)<n>We propose a novel multi-level motion framework for AQA on enhanced spatial-temporal pose features.<n>Our framework achieves state-of-the-art performance in both action segmentation and action scoring tasks.
• Score: 7.956640274660439
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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