TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?
Zhongyuan Bao, Lejun Zhang
Published: 2025/9/19
Abstract
Multimodal large language models (MLLMs) excel at general video understanding but struggle with fast, high-frequency sports like tennis, where rally clips are short yet information-dense. To systematically evaluate MLLMs in this challenging domain, we present TennisTV, the first and most comprehensive benchmark for tennis video understanding. TennisTV models each rally as a temporal-ordered sequence of consecutive stroke events, using automated pipelines for filtering and question generation. It covers 8 tasks at rally and stroke levels and includes 2,500 human-verified questions. Evaluating 16 representative MLLMs, we provide the first systematic assessment of tennis video understanding. Results reveal substantial shortcomings and yield two key insights: (i) frame-sampling density should be tailored and balanced across tasks, and (ii) improving temporal grounding is essential for stronger reasoning.