Abstract
In sports training, personalized skill assessment and feedback are crucial for athletes to master complex movements and improve performance. However, existing research on skill transfer predominantly focuses on skill evaluation through video analysis, addressing only a single facet of the multifaceted process required for skill acquisition. Furthermore, in the limited studies that generate expert comments, the learner's skill level is predetermined, and the spatial-temporal information of human movement is often overlooked. To address this issue, we propose a novel approach to generate skill-level-aware expert comments by leveraging a Large Multimodal Model (LMM) and spatial-temporal motion features. Our method employs a Spatial-Temporal Attention Graph Convolutional Network (STA-GCN) to extract motion features that encapsulate the spatial-temporal dynamics of human movement. The STA-GCN classifies skill levels based on these motion features. The classified skill levels, along with the extracted motion features (intermediate features from the STA-GCN) and the original sports video, are then fed into the LMM. This integration enables the generation of detailed, context-specific expert comments that offer actionable insights for performance improvement. Our contributions are twofold: (1) We incorporate skill level classification results as inputs to the LMM, ensuring that feedback is appropriately tailored to the learner's skill level; and (2) We integrate motion features that capture spatial-temporal information into the LMM, enhancing its ability to generate feedback based on the learner's specific actions. Experimental results demonstrate that the proposed method effectively generates expert comments, overcoming the limitations of existing methods and offering valuable guidance for athletes across various skill levels.