Year: 2026
Type: article
Abstract: Action quality assessment aims to evaluate how well an action is performed and has important potential applications in sports training and instructional feedback. However, video-based assessment methods may be distracted by complex backgrounds and may insufficiently capture fine-grained body configurations that are critical for distinguishing subtle differences in movement execution. To address these limitations, this study proposes a Pose-Aware Visual Interaction Network, termed PAVI-Net, for sports action quality assessment. PAVI-Net adopts a dual-stream architecture in which an RGB video stream captures global appearance and spatiotemporal motion, while a pose stream models body-joint configurations and motion dynamics. A pose-aware visual interaction module is introduced to use pose features as queries and video features as keys and values, enabling pose-guided refinement of visual representations through cross-attention. Unlike direct feature concatenation, the proposed interaction allows body-structure information to actively guide the selection of action-relevant visual features and reduce the influence of unrelated contextual regions. Experiments on the MTL-AQA and AQA-7 benchmark datasets demonstrate that PAVI-Net achieves a Spearman correlation of 0.9536 on MTL-AQA under the difficulty-degree setting and an average correlation of 0.8421 on AQA-7. Ablation results further show that pose-aware visual interaction improves performance over both the video-only baseline and direct video–pose concatenation. Nevertheless, the framework remains dependent on pose-estimation quality, and its performance may decrease under severe occlusion, rapid motion, low human visibility, or substantial domain differences. Therefore, the present results demonstrate the effectiveness of PAVI-Net on benchmark sports videos, while further validation is required before deployment in authentic sports teaching environments.
Source: Scientific Reports
Authors: Yi-nan Gu, Dongxu Gao, Lu Li
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,690
OpenAlex ID: W7203832856
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