# TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation

arXiv: https://arxiv.org/abs/2504.17365 · HTML: https://mlsanity.com/paper/2504.17365/

## Key facts

- Cited by 13 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.
- Builds on 31 of its 43 references that are in the map.
- First posted on arXiv 24 Apr 2025.

## Placed in

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

## Builds on (most cited first, 30 of 31)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [Qwen2.5-VL Technical Report](https://mlsanity.com/paper/2502.13923/) (2025)
- [LLaVA-OneVision: Easy Visual Task Transfer](https://mlsanity.com/paper/2408.03326/) (2024)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Video-LLaMA: An Instruction-tuned Audio-Visual Language Model for Video Understanding](https://mlsanity.com/paper/2306.02858/) (2023)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [Video-ChatGPT: Towards Detailed Video Understanding via Large Vision and Language Models](https://mlsanity.com/paper/2306.05424/) (2023)
- [Token Merging: Your ViT But Faster](https://mlsanity.com/paper/2210.09461/) (2022)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [Dense-Captioning Events in Videos](https://mlsanity.com/paper/1705.00754/) (2017)
- [LLaVA-Video: Video Instruction Tuning With Synthetic Data](https://mlsanity.com/paper/2410.02713/) (2024)
- [MovieChat: From Dense Token to Sparse Memory for Long Video Understanding](https://mlsanity.com/paper/2307.16449/) (2023)
- [XMem: Long-Term Video Object Segmentation with an Atkinson-Shiffrin Memory Model](https://mlsanity.com/paper/2207.07115/) (2022)
- [TimeChat: A Time-sensitive Multimodal Large Language Model for Long Video Understanding](https://mlsanity.com/paper/2312.02051/) (2023)
- [Chat-UniVi: Unified Visual Representation Empowers Large Language Models with Image and Video Understanding](https://mlsanity.com/paper/2311.08046/) (2023)
- [Vid2Seq: Large-Scale Pretraining of a Visual Language Model for Dense Video Captioning](https://mlsanity.com/paper/2302.14115/) (2023)
- [VideoAgent: A Memory-augmented Multimodal Agent for Video Understanding](https://mlsanity.com/paper/2403.11481/) (2024)
- [SoccerNet: A Scalable Dataset for Action Spotting in Soccer Videos](https://mlsanity.com/paper/1804.04527/) (2018)
- [SoccerNet-v2: A Dataset and Benchmarks for Holistic Understanding of Broadcast Soccer Videos](https://mlsanity.com/paper/2011.13367/) (2020)
- [TimeSuite: Improving MLLMs for Long Video Understanding via Grounded Tuning](https://mlsanity.com/paper/2410.19702/) (2024)
- [SoccerNet-Tracking: Multiple Object Tracking Dataset and Benchmark in Soccer Videos](https://mlsanity.com/paper/2204.06918/) (2022)
- [Dynamic-LLaVA: Efficient Multimodal Large Language Models via Dynamic Vision-language Context Sparsification](https://mlsanity.com/paper/2412.00876/) (2024)
- [SoccerNet-Caption: Dense Video Captioning for Soccer Broadcasts Commentaries](https://mlsanity.com/paper/2304.04565/) (2023)
- [MatchTime: Towards Automatic Soccer Game Commentary Generation](https://mlsanity.com/paper/2406.18530/) (2024)
- [What Kind of Visual Tokens Do We Need? Training-free Visual Token Pruning for Multi-modal Large Language Models from the Perspective of Graph](https://mlsanity.com/paper/2501.02268/) (2025)
- [SoccerNet 2023 Challenges Results](https://mlsanity.com/paper/2309.06006/) (2023)
- [Multimodal and multiview distillation for real-time player detection on a football field](https://mlsanity.com/paper/2004.07544/) (2020)
- [LLaVA-RadZ: Can Multimodal Large Language Models Effectively Tackle Zero-shot Radiology Recognition?](https://mlsanity.com/paper/2503.07487/) (2025)

## Built on by (most cited first, 13 of 13)

- [CompBench: Benchmarking Complex Instruction-guided Image Editing](https://mlsanity.com/paper/2505.12200/) (2025)
- [SoccerMaster: A Vision Foundation Model for Soccer Understanding](https://mlsanity.com/paper/2512.11016/) (2025)
- [SmartSight: Mitigating Hallucination in Video-LLMs Without Compromising Video Understanding via Temporal Attention Collapse](https://mlsanity.com/paper/2512.18671/) (2025)
- [DeepSport: A Multimodal Large Language Model for Comprehensive Sports Video Reasoning via Agentic Reinforcement Learning](https://mlsanity.com/paper/2511.12908/) (2025)
- [From Multimodal Perception to Strategic Reasoning: A Survey on AI-Generated Game Commentary](https://mlsanity.com/paper/2506.17294/) (2025)
- [TennisExpert: Towards Expert-Level Analytical Sports Video Understanding](https://mlsanity.com/paper/2603.13397/) (2026)
- [Towards Automatic Soccer Commentary Generation with Knowledge-Enhanced Visual Reasoning](https://mlsanity.com/paper/2604.00057/) (2026)
- [RefereeBench: Are Video MLLMs Ready to be Multi-Sport Referees](https://mlsanity.com/paper/2604.15736/) (2026)
- [Where is the multimodal goal post? On the Ability of Foundation Models to Recognize Highlights in Videos](https://mlsanity.com/paper/2601.16333/) (2026)
- [BoxComm: Benchmarking Category-Aware Commentary Generation and Narration Rhythm in Boxing](https://mlsanity.com/paper/2604.04419/) (2026)
- [SoccerRef-Agents: Multi-Agent System for Automated Soccer Refereeing](https://mlsanity.com/paper/2604.23392/) (2026)
- [A Survey of Large Models in Sports](https://mlsanity.com/paper/2608.14377/) (2026)
- [StreamSoccer: Event-Driven Memory for Streaming Soccer Commentary](https://mlsanity.com/paper/2608.19723/) (2026)

## Abstract (arXiv)

Soccer is a globally popular sporting event, typically characterized by long matches and distinctive highlight moments. Recent advances in Multimodal Large Language Models (MLLMs) offer promising capabilities in temporal grounding and video understanding, soccer commentary generation often requires precise temporal localization and semantically rich descriptions over long-form video. However, existing soccer MLLMs often rely on the temporal a priori for caption generation, so they cannot process the soccer video end-to-end. While some traditional approaches follow a two-step paradigm that is complex and fails to capture the global context to achieve suboptimal performance. To solve the above issues, we present TimeSoccer, the first end-to-end soccer MLLM for Single-anchor Dense Video Captioning (SDVC) in full-match soccer videos. TimeSoccer jointly predicts timestamps and generates captions in a single pass, enabling global context modeling across 45-minute matches. To support long video understanding of soccer matches, we introduce MoFA-Select, a training-free, motion-aware frame compression module that adaptively selects representative frames via a coarse-to-fine strategy, and incorporates complementary training paradigms to strengthen the model's ability to handle long temporal sequences. Extensive experiments demonstrate that our TimeSoccer achieves State-of-The-Art (SoTA) performance on the SDVC task in an end-to-end form, generating high-quality commentary with accurate temporal alignment and strong semantic relevance.