TimeSoccer: An End-to-End Multimodal Large Language Model for Soccer Commentary Generation (2504.17365v3)

Abstract: Soccer is a globally popular sporting event, typically characterized by long matches and distinctive highlight moments. Recent advances in Multimodal LLMs (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.

TimeSoccer: An End-to-End Multimodal LLM for Soccer Commentary Generation

The paper "TimeSoccer: An End-to-End Multimodal LLM for Soccer Commentary Generation" presents a novel approach to generating commentary for soccer matches by leveraging a multimodal LLM (MLLM) architecture. Identifying the challenges in processing lengthy video content with precise temporal alignment, this research proposes a comprehensive solution with TimeSoccer, which strives to enhance both temporal grounding and semantic richness in soccer commentary generation.

Key Contributions

1. Model Architecture: TimeSoccer is introduced as the first end-to-end model tailored for Single-anchor Dense Video Captioning (SDVC) specifically in full-length soccer matches. Its architecture jointly predicts event timestamps and generates descriptions from video in a single pass, contrasting with traditional two-step paradigms that optimize timestamps and captions separately — a design consequentially foregoing global context understanding.
2. MoFA-Select Module: The paper details MoFA-Select, a motion-aware frame compression module highlighted by its ability to select the most representative frames within a long video input through a coarse-to-fine strategy. This module adapts frame selection based on semantic importance and motion sensitivity, realizing efficient temporal modeling over 45-minute soccer matches devoid of substantial resource overheads or traditional memory-heavy designs.
3. Progressive Training Strategy: The research incorporates a progressive learning strategy to intensify the model's comprehension of long temporal sequences efficiently. Through incrementally expanded video training lengths coupled with position embedding strategies, TimeSoccer operates effectively on video sequences far beyond typical input constraints, maintaining semantic coherence across extensive temporal scopes.

Experimental Outcomes

The experimental results showcased TimeSoccer's superiority in state-of-the-art (SoTA) performance across both temporal localization and caption quality. The evaluations on the SoccerNet-Caption dataset demonstrated marked improvements:

• Temporal Metrics: TimeSoccer achieved significant advances in temporal precision with higher scores in [email protected], [email protected], and [email protected], enhancing accurate soccer event spotting over its predecessor, SN-Caption.
• Caption Quality: The caption output outperformed existing models in CIDEr, METEOR, and SODA_c metrics. Commentaries were evaluated for match-level semantic alignment using Qwen2.5-VL-72B-Instruct boosting overall commentary quality.

Practical and Theoretical Implications

The research holds meaningful implications in both practical soccer analytics and theoretical advancements in video understanding. Practically, TimeSoccer provides scalable solutions for real-time commentary generation in sports broadcasting and media analysis. Theoretically, the paper sets a foundation for integrating time-aware frame compression and progressive learning in broader temporal sequence challenges outside of soccer, potentially encompassing diverse domains requiring real-time events analysis with strong semantic congruity.

Future Directions

TimeSoccer opens up avenues for further exploration in MLLMs with intense focus on holistic video-event models. Future developments could delve into refining multimodal models that extend beyond sports, handling broader categories of live content streams with diversified grounding techniques and enriched narrative comprehension. Furthermore, an integration of reinforcement learning methods to optimize both commentary relevance and viewer engagement during live streams or automated broadcast systems may offer intriguing advancements in AI-driven sportscasting.

In conclusion, TimeSoccer addresses critical gaps in soccer commentary generation by constructing an efficient and comprehensive framework complementing both accuracy in temporal descriptions and semantic engagement. The research stands as a significant contribution within AI and multimodal understanding, offering solid methodological insights for future developments in complex video analysis tasks.