TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies?
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866913037698465792 |
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| author | Bao, Zhongyuan Zhang, Lejun |
| author_facet | Bao, Zhongyuan Zhang, Lejun |
| contents | 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 from the stroke level to the rally level and includes 2527 human-verified questions. Evaluating 17 representative MLLMs, we provide the first systematic assessment of tennis video understanding. Results 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2509_15602 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies? Bao, Zhongyuan Zhang, Lejun Computer Vision and Pattern Recognition 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 from the stroke level to the rally level and includes 2527 human-verified questions. Evaluating 17 representative MLLMs, we provide the first systematic assessment of tennis video understanding. Results 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. |
| title | TennisTV: Do Multimodal Large Language Models Understand Tennis Rallies? |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2509.15602 |