构建首个滑冰艺术理解基准，推动模型理解技术与表演的融合。
FSBench: A Figure Skating Benchmark for Advancing Artistic Sports Understanding
- 构建多模态滑冰数据集FSBench，支持技术分析到表演评论任务。
- 现有模型在艺术体育理解上表现差，尤其在综合判断上能力不足。
- 适合研究艺术类运动理解、多模态问答与智能裁判系统的学者。
花样滑冰被誉为“冰上艺术”，是高度艺术化的体育项目，其理解难度源于技术动作（如跳跃、旋转）与整体艺术表现的结合。现有滑冰数据集多聚焦单一任务，如动作识别或评分，缺乏对技术和艺术评价的全面标注。当前体育研究主要集中于球类运动，对艺术类体育关注有限。为此，我们提出FSAnno——一个大规模数据集，旨在推进艺术类体育的理解。该数据集包含开放训练与测试数据，以及用于公平评估的基准数据集FSBench。FSBench包含FSBench-Text（多项选择题与解释）和FSBench-Motion（多模态数据与问答对），支持从技术分析到表演评论的任务。初步测试表明，现有模型在艺术体育理解方面存在显著局限。我们期望FSBench成为评估与提升模型对花样滑冰理解的关键工具。
原文摘要 · Abstract (English)
Figure skating, known as the "Art on Ice," is among the most artistic sports, challenging to understand due to its blend of technical elements (like jumps and spins) and overall artistic expression. Existing figure skating datasets mainly focus on single tasks, such as action recognition or scoring, lacking comprehensive annotations for both technical and artistic evaluation. Current sports research is largely centered on ball games, with limited relevance to artistic sports like figure skating. To address this, we introduce FSAnno, a large-scale dataset advancing artistic sports understanding through figure skating. FSAnno includes an open-access training and test dataset, alongside a benchmark dataset, FSBench, for fair model evaluation. FSBench consists of FSBench-Text, with multiple-choice questions and explanations, and FSBench-Motion, containing multimodal data and Question and Answer (QA) pairs, supporting tasks from technical analysis to performance commentary. Initial tests on FSBench reveal significant limitations in existing models' understanding of artistic sports. We hope FSBench will become a key tool for evaluating and enhancing model comprehension of figure skating.
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