MPTc-Bench: Measuring Cross-market Generative Ability of Vision-Language Models via Movie Poster Transcreation

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 a VLM planner specifies executable edits and an image editor renders them. We evaluate in a triplet setup (source, human targetmarket poster, model output) using informationpreservation 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 targetmarket posters, highlighting open challenges for market-aware generation. MPTc-Bench enables controlled, quantitative progress on crossmarket image editing beyond understandingcentric benchmarks. 1 1 Code and dataset: minamotoorin.github.io/mptc-bench.