Abstract
Recent multimodal large language models (MLLMs) have shown strong capabilities in general video understanding, driving growing interest in automatic sports commentary generation. However, existing benchmarks for this task focus exclusively on team sports such as soccer and basketball, leaving combat sports entirely unexplored. Notably, combat sports present distinct challenges: critical actions unfold within milliseconds with visually subtle yet semantically decisive differences, and professional commentary contains a substantially higher proportion of tactical analysis compared to team sports. In this paper, we present BoxComm, a large-scale dataset comprising 445 World Boxing Championship match videos with over 52K commentary sentences from professional broadcasts. We propose a structured commentary taxonomy that categorizes each sentence into play-by-play, tactical, or contextual, providing the first category-level annotation for sports commentary benchmarks. Building on this taxonomy, we introduce two novel and complementary evaluations tailored to sports commentary generation: (1) category-conditioned generation, which evaluates whether models can produce accurate commentary of a specified type given video context; and (2) commentary rhythm assessment, which measures whether freely generated commentary exhibits appropriate temporal pacing and type distribution over continuous video segments, capturing a dimension of commentary competence that prior benchmarks have not addressed. Experiments on multiple state-of-the-art MLLMs reveal that current models struggle on both evaluations. We further propose EIC-Gen, an improved baseline incorporating detected punch events to supply structured action cues, yielding consistent gains and highlighting the importance of perceiving fleeting and subtle events for combat sports commentary.
Abstract (translated)
近期多模态大语言模型（MLLMs）在通用视频理解方面展现出强大能力，推动了自动体育赛事解说生成研究的兴起。然而，现有相关基准数据集仅聚焦于足球、篮球等团队运动，完全未涉及格斗类项目。值得注意的是，格斗运动存在独特挑战：关键动作在毫秒级时间内发生，视觉特征细微但语义区分度极高；且专业解说中战术分析占比显著高于团队运动。本文提出BoxComm大规模数据集，包含445场世界拳击锦标赛视频及超过5.2万条专业转播解说语句。我们设计结构化解说分类体系，将每条解说划分为现场播报、战术分析或背景说明三类，首次为体育解说基准提供细粒度标注。基于该体系，我们引入两种互补的新型评估方法：一是类别条件生成测试，评估模型能否根据视频语境生成指定类型的准确解说；二是解说节奏评估，衡量自由生成的解说在连续视频片段中是否具备恰当的时间分布与类型配比，涵盖既往基准未触及的解说能力维度。在多个先进MLLMs上的实验表明，当前模型在两个评估上均表现不佳。进一步提出EIC-Gen改进基线，通过检测出拳事件提供结构化动作线索，实现稳定性能提升，凸显感知瞬时细微事件对格斗运动解说的重要性。
URL
https://arxiv.org/abs/2604.04419
