MuseBench：多模态大语言模型中意图级视听艺术理解的基准测试

摘要

视听艺术涵盖多种创意学科，包括电影、视觉艺术、舞台表演和游戏设计，其艺术意义源于对视觉、听觉与叙事元素的刻意组合（例如，通过幽闭式构图放大恐惧感，或通过静默与长时间特写镜头传达悲伤）。真正的艺术理解不仅在于识别画面所描绘的内容，更在于推理为何通过特定的创作手法来表达。尽管多模态大语言模型取得了显著进展，但这一艺术理解的关键方面仍未被充分探索——现有基准测试主要衡量感知层面的识别能力，却忽视了创作意图的推理。为弥补这一空白，我们提出Musebench，一个用于评估多模态大语言模型在细微艺术理解方面能力的全面基准测试。该基准包含4016道题目，涵盖电影艺术、静态视觉艺术、舞台表演艺术与游戏艺术，从超过1万部结合专业评论与视觉演示的候选视频论文中提炼而成。为在大规模上捕捉艺术分析的开放性特点，基准测试结合了单项选择与可变选项的多项选择。所有题目均通过四阶段迭代流程生成与优化，该流程结合了快捷筛选、对抗性干扰项与专家验证。对28个最先进多模态大语言模型进行的全面零样本评估显示，即使性能最佳的模型也仅达到48.29%的准确率，远低于人类专家的87.18%，暴露出当前模型在创意领域专业知识方面的显著差距。

English

Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning arises from deliberate combinations of visual, auditory, and narrative elements (e.g., fear amplified through claustrophobic framing, or grief conveyed through silence and lingering close-ups). True artistic understanding extends beyond recognizing what is depicted to reasoning about why it is expressed through particular creative choices. Despite the strong progress of multimodal large language models (MLLMs), this critical aspect of artistic understanding remains underexplored, as existing benchmarks largely measure perceptual recognition while overlooking reasoning about creative intent. To address this gap, we introduce Musebench, a comprehensive benchmark designed to evaluate MLLMs on nuanced artistic understanding. It comprises 4,016 questions spanning cinematic arts, static visual arts, stage performing arts, and game arts, distilled from over 10K candidate video essays that pair professional commentary with visual demonstration. To capture the open-ended nature of artistic analysis at scale, the benchmark combines single-select and variable-option multi-select questions. All questions are generated and refined through a four-phase iterative pipeline combining shortcut filtering, adversarial distractors, and expert validation. Comprehensive zero-shot evaluation of 28 state-of-the-art MLLMs reveals that even the best-performing model achieves only 48.29% accuracy, substantially below human expert performance of 87.18%, exposing a significant gap in current models' creative domain expertise.