# FineBadminton: A Multi-Level Dataset for Fine-Grained Badminton Video Understanding

arXiv: https://arxiv.org/abs/2508.07554 · HTML: https://mlsanity.com/paper/2508.07554/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Placed in Embodied and 3D vision-language models, one of 16 directions in Multimodal LLMs / vision-language models.
- First posted on arXiv 11 Aug 2025.

## Placed in

- Multimodal LLMs / vision-language models / [Embodied and 3D vision-language models](https://mlsanity.com/mllm/embodied-and-3d-vision-language-models/)

## Built on by (most cited first, 10 of 10)

- [SportR: A Benchmark for Multimodal Large Language Model Reasoning in Sports](https://mlsanity.com/paper/2511.06499/) (2025)
- [Stepping VLMs onto the Court: Benchmarking Spatial Intelligence in Sports](https://mlsanity.com/paper/2603.09896/) (2026)
- [Learning Consistent Temporal Grounding between Related Tasks in Sports Coaching](https://mlsanity.com/paper/2603.18453/) (2026)
- [BFMD: A Full-Match Badminton Dense Dataset for Dense Shot Captioning](https://mlsanity.com/paper/2603.25533/) (2026)
- [RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees](https://mlsanity.com/paper/2604.15736/) (2026)
- [Beyond the Single Camera: Agentic Multi-View Reasoning in Sports Video Understanding](https://mlsanity.com/paper/2607.11844/) (2026)
- [BoxComm: Benchmarking Category-Aware Commentary Generation and Narration Rhythm in Boxing](https://mlsanity.com/paper/2604.04419/) (2026)
- [BadmintonGRF: A Multimodal Dataset and Benchmark for Markerless Ground Reaction Force Estimation in Badminton](https://mlsanity.com/paper/2605.01876/) (2026)
- [A Survey of Large Models in Sports](https://mlsanity.com/paper/2608.14377/) (2026)
- [DoublesEval: Diagnosing Multi-Agent Tactical Reasoning in Vision-Language Models via Professional Doubles Badminton](https://mlsanity.com/paper/2608.24439/) (2026)

## Abstract (arXiv)

Fine-grained analysis of complex and high-speed sports like badminton presents a significant challenge for Multimodal Large Language Models (MLLMs), despite their notable advancements in general video understanding. This difficulty arises primarily from the scarcity of datasets with sufficiently rich and domain-specific annotations. To bridge this gap, we introduce FineBadminton, a novel and large-scale dataset featuring a unique multi-level semantic annotation hierarchy (Foundational Actions, Tactical Semantics, and Decision Evaluation) for comprehensive badminton understanding. The construction of FineBadminton is powered by an innovative annotation pipeline that synergistically combines MLLM-generated proposals with human refinement. We also present FBBench, a challenging benchmark derived from FineBadminton, to rigorously evaluate MLLMs on nuanced spatio-temporal reasoning and tactical comprehension. Together, FineBadminton and FBBench provide a crucial ecosystem to catalyze research in fine-grained video understanding and advance the development of MLLMs in sports intelligence. Furthermore, we propose an optimized baseline approach incorporating Hit-Centric Keyframe Selection to focus on pivotal moments and Coordinate-Guided Condensation to distill salient visual information. The results on FBBench reveal that while current MLLMs still face significant challenges in deep sports video analysis, our proposed strategies nonetheless achieve substantial performance gains. The project homepage is available at https://finebadminton.github.io/FineBadminton/.