title: Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural
    Understanding and Transcreation
summary: 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.
paper_authors._id: 684009f8a33aeee1125b5765
paper_authors.name: Li Zhou
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2025-06-07T05:49:28.567Z
paper_authors._id: 63d925b189a9683b46d33e89
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/63d925b189a9683b46d33e89/lYFvL-rLytMVaWVlfGkL_.jpeg
paper_authors.fullname: Li Zhou
paper_authors.type: user
paper_authors.user: lizhou21
paper_authors._id: 684009f8a33aeee1125b5766
paper_authors.name: Lutong Yu
paper_authors._id: 684009f8a33aeee1125b5767
paper_authors.name: Dongchu Xie
paper_authors._id: 684009f8a33aeee1125b5768
paper_authors.name: Shaohuan Cheng
paper_authors._id: 684009f8a33aeee1125b5769
paper_authors.name: Wenyan Li
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2025-06-05T07:36:16.451Z
paper_authors._id: 619b506f70d03780cbec5806
paper_authors.avatarUrl: /avatars/ea2b0b8f0a3eb16d53ef40da9981c397.svg
paper_authors.fullname: wenyan li
paper_authors.type: user
paper_authors.user: lyan62
paper_authors._id: 684009f8a33aeee1125b576a
paper_authors.name: Haizhou Li
paper_title: Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural
    Understanding and Transcreation
paper_summary: 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.
paper_ai_summary: Hanfu-Bench, a new expert-curated dataset, addresses temporal aspects of cultural understanding and creative adaptation using vision-language models, highlighting significant challenges in these tasks.
paper_ai_keywords: vision-language models multimodal dataset cultural visual understanding cultural image transcreation visual question answering cultural element inheritance modern context adaptation