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    <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: Surface (text-centric localization) and 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.</abstract>
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%0 Conference Proceedings
%T MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation
%A Lin, Youyuan
%A Li, Yuan
%A Yu, Yahan
%A Cheng, Fei
%A Nishida, Shin’ya
%A Chu, Chenhui
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F lin-etal-2026-mptc
%X 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: Surface (text-centric localization) and 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.
%U https://aclanthology.org/2026.findings-acl.1889/
%P 37897-37913

Markdown (Informal)

[MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation](https://aclanthology.org/2026.findings-acl.1889/) (Lin et al., Findings 2026)

• MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation (Lin et al., Findings 2026)

ACL

• Youyuan Lin, Yuan Li, Yahan Yu, Fei Cheng, Shin’ya Nishida, and Chenhui Chu. 2026. MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation. In Findings of the Association for Computational Linguistics: ACL 2026, pages 37897–37913, San Diego, California, United States. Association for Computational Linguistics.