Generating Tennis Action Instruction Based on a Large Language Model

Zhiyuan Wang, Zhiqian Xia, Guangyao Zhao, Kaiyue Lu, Haifeng Xia, Siyu Xia; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 6557-6566

As tennis continues to gain global popularity, there is a growing need for precise, scalable, and standardized training solutions. However, the evaluation and instruction of tennis techniques remain largely dependent on subjective assessments by experienced coaches, limiting consistency and automation. In this paper, we present TAGS (Tennis Action Guidance System), a unified framework that leverages large language models to generate expert-level evaluations and actionable feedback for tennis performance. Given a tennis video and a user-defined query, TAGS first extracts 3D skeletal keypoints, encodes the motion into a structured representation, and employs prompt-based LLMs to assess performance across five critical dimensions: stability, coordination, power generation, technical execution, and rhythm. To support this process, we introduce (1) a pose-language unified representation that aligns 3D motion data with natural language understanding, (2) structured prompt templates grounded in professional coaching expertise, and (3) a multi-dimensional evaluation protocol incorporating both human and automated assessments. In addition, we contribute the first large-scale dataset for tennis action evaluation, comprising 3,000 video-query-feedback triplets authored by certified coaches. Extensive experiments and user studies demonstrate that TAGS consistently outperforms existing baselines in delivering accurate, domain-specific, and actionable feedback. This work marks a step forward in developing interpretable, human-aligned systems for real-world sports action assessment and intelligent coaching.