@inproceedings{lin-etal-2026-mptc,
    title = "{MPT}c-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation",
    author = "Lin, Youyuan  and
      Li, Yuan  and
      Yu, Yahan  and
      Cheng, Fei  and
      Nishida, Shin'ya  and
      Chu, Chenhui",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.findings-acl.1889/",
    pages = "37897--37913",
    ISBN = "979-8-89176-395-1",
    abstract = "Generative vision-language models (VLMs) can edit and synthesize images, yet their ability to adapt visual assets across markets remains under-evaluated.We study cross-market image transcreation via movie posters, where localization must preserve a movie{'}s identity while matching market-specific design preferences and multilingual typography.We introduce the Movie Poster Transcreation Benchmark (MPTc-Bench), a cross-market benchmark of 582 aligned poster examples spanning 34 target markets, and define two task variants: $\textbf{Surface}$ (text-centric localization) and $\textbf{Deep}$ (preference-level style adaptation).We propose a two-stage planner-editor pipeline in which an VLM planner specifies executable edits and an image editor renders them.We evaluate in a triplet setup (source, human target-market poster, model output) using information-preservation checks, LLM-as-a-judge ratings for aesthetics and target-market fit, and objective similarity signals.Across multiple planners and editors, experiments reveal substantial gaps between model outputs and human target-market posters, highlighting open challenges for market-aware generation.MPTc-Bench enables controlled, quantitative progress on cross-market image editing beyond understanding-centric benchmarks."
}