汉服基准：跨时代文化理解与再创作的多模态评估体系

Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation

June 2, 2025

作者: Li Zhou, Lutong Yu, Dongchu Xie, Shaohuan Cheng, Wenyan Li, Haizhou Li

cs.AI

摘要

文化是一个丰富且动态的领域，其演变跨越地理与时间维度。然而，现有利用视觉-语言模型（VLMs）进行文化理解的研究主要聚焦于地理多样性，往往忽视了关键的时间维度。为填补这一空白，我们推出了Hanfu-Bench，一个由专家精心策划的多模态数据集。汉服，作为贯穿中国古代各朝代的传统服饰，是反映中国文化深厚时间维度的代表性文化遗产，同时在当代中国社会中仍享有极高的人气。Hanfu-Bench包含两大核心任务：文化视觉理解与文化图像转译。前者通过多选视觉问答考察基于单张或多张图像输入的时间-文化特征识别能力，后者则侧重于通过文化元素传承与现代语境适应，将传统服饰转化为现代设计。评估结果显示，在文化视觉理解任务上，封闭式VLMs的表现与非专家相当，但与人类专家相比仍有10%的差距，而开放式VLMs则进一步落后于非专家。在转译任务中，多维度的人类评估表明，表现最佳的模型成功率仅为42%。我们的基准测试为这一新兴的时间文化理解与创意适应方向提供了重要的实验平台，揭示了其中存在的重大挑战。

English

Culture is a rich and dynamic domain that evolves across both geography and time. However, existing studies on cultural understanding with vision-language models (VLMs) primarily emphasize geographic diversity, often overlooking the critical temporal dimensions. To bridge this gap, we introduce Hanfu-Bench, a novel, expert-curated multimodal dataset. Hanfu, a traditional garment spanning ancient Chinese dynasties, serves as a representative cultural heritage that reflects the profound temporal aspects of Chinese culture while remaining highly popular in Chinese contemporary society. Hanfu-Bench comprises two core tasks: cultural visual understanding and cultural image transcreation.The former task examines temporal-cultural feature recognition based on single- or multi-image inputs through multiple-choice visual question answering, while the latter focuses on transforming traditional attire into modern designs through cultural element inheritance and modern context adaptation. Our evaluation shows that closed VLMs perform comparably to non-experts on visual cutural understanding but fall short by 10\% to human experts, while open VLMs lags further behind non-experts. For the transcreation task, multi-faceted human evaluation indicates that the best-performing model achieves a success rate of only 42\%. Our benchmark provides an essential testbed, revealing significant challenges in this new direction of temporal cultural understanding and creative adaptation.