  Given the rise of multimedia content, human translators increasingly focus on
culturally adapting not only words but also other modalities such as images to
convey the same meaning. While several applications stand to benefit from this,
machine translation systems remain confined to dealing with language in speech
and text. In this work, we take a first step towards translating images to make
them culturally relevant. First, we build three pipelines comprising
state-of-the-art generative models to do the task. Next, we build a two-part
evaluation dataset: i) concept: comprising 600 images that are cross-culturally
coherent, focusing on a single concept per image, and ii) application:
comprising 100 images curated from real-world applications. We conduct a
multi-faceted human evaluation of translated images to assess for cultural
relevance and meaning preservation. We find that as of today, image-editing
models fail at this task, but can be improved by leveraging LLMs and retrievers
in the loop. Best pipelines can only translate 5% of images for some countries
in the easier concept dataset and no translation is successful for some
countries in the application dataset, highlighting the challenging nature of
the task. Our code and data is released here:
https://github.com/simran-khanuja/image-transcreation.
