Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural
  Understanding and Transcreation
Authors: name: Li Zhou, name: Lutong Yu, name: Dongchu Xie, name: Shaohuan Cheng, name: Wenyan Li, name: Haizhou Li

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.