| --- |
| license: apache-2.0 |
| task_categories: |
| - visual-question-answering |
| language: |
| - en |
| size_categories: |
| - 1M<n<10M |
| --- |
| |
| <h1><img width="4%"/><i>Stepping VLMs onto the Court</i>: Benchmarking Spatial Intelligence in Sports</h1> |
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| <a href="https://arxiv.org/abs/2603.09896" target="_blank"> |
| <img alt="arXiv" src="https://img.shields.io/badge/arXiv-CourtSI-red?logo=arxiv" height="20" /> |
| </a> |
| <a href="https://visionary-laboratory.github.io/CourtSI/" target="_blank"> |
| <img alt="Website" src="https://img.shields.io/badge/🌎_Website-CourtSI-blue.svg" height="20" /> |
| </a> |
| |
| <a href="https://github.com/Visionary-Laboratory/CourtSI" target="_blank"> |
| <img alt="GitHub Repo" src="https://img.shields.io/badge/GitHub-CourtSI-black?logo=github" height="20" /> |
| </a> |
| |
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| ## Abstract |
| Sports have long attracted broad attention as they push the limits of human physical and cognitive capabilities. Amid growing interest in spatial intelligence for vision-language models (VLMs), sports provide a natural testbed for understanding high-intensity human motion and dynamic object interactions. To this end, we present CourtSI, the first large-scale spatial intelligence dataset tailored to sports scenarios. CourtSI contains over 1M QA pairs, organized under a holistic taxonomy that systematically covers spatial counting, distance measurement, localization, and relational reasoning, across representative net sports including badminton, tennis, and table tennis. Leveraging well-defined court geometry as metric anchors, we develop a semi-automatic data engine to reconstruct sports scenes, enabling scalable curation of CourtSI. In addition, we introduce CourtSI-Bench, a high-quality evaluation benchmark comprising 3,686 QA pairs with rigorous human verification. We evaluate 25 proprietary and open-source VLMs on CourtSI-Bench, revealing a remaining human–AI performance gap and limited generalization from existing spatial intelligence benchmarks. These findings indicate that sports scenarios expose limitations in spatial intelligence capabilities captured by existing benchmarks. Further, fine-tuning Qwen3-VL-8B on CourtSI improves accuracy on CourtSI-Bench by 23.5 percentage points. The adapted model also generalizes effectively to CourtSI-Ext, an evaluation set built on a similar but unseen sport, and demonstrates enhanced spatial-aware commentary generation. Together, these findings demonstrate that CourtSI provides a scalable pathway toward advancing spatial intelligence of VLMs in sports. |
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| ## CourtSI |
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| CourtSI is a large-scale dataset designed to study spatial intelligence in sports environments for Vision-Language Models (VLMs). |
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| It provides large-scale training data for supervised fine-tuning (SFT) of vision-language models. The dataset contains over **1M QA pairs**, built upon a holistic spatial taxonomy that includes **spatial counting, distance measurement, localization, and relational reasoning**. |
