CultureScore: Evaluating Cultural Faithfulness in Video Generation Models

arXiv:2606.07311v3 Announce Type: replace-cross Abstract: As video generation models like Veo 3.1 and LTX-2 advance, their ability to accurately represent diverse global cultures remains a critical yet understudied frontier. Current metrics, such as VideoScore, only measure visual quality but offer no mechanism for assessing cultural faithfulness. Consequently, a model that replaces a Namaste with a handshake receives the same score as one that generates the gesture correctly. We propose CultureScore, a compositional evaluation framework that decomposes cultural faithfulness into three granular dimensions: Identity (who is represented), Context (culturally localized background), and Behavior (normative gestures and interactions). We operationalize this framework through an evaluation suite spanning 10 countries, yielding 6,174 generated videos across three state-of-the-art models. Our evaluation reveals that no current model achieves culturally faithful video generation: the best-performing model reaches only 56.8% overall CultureScore, with Behavior the most challenging dimension; no model exceeds 52.1% on behavior. Furthermore, the highest-scoring model (LTX-2) on visual quality was ranked last by native annotators, while CultureScore's Behavior dimension shows the strongest positive correlation with human cultural judgment among the automatic metrics we evaluate, underscoring that cultural faithfulness is an essential criterion for equitable video generation. Data and code are publicly available.\footnote{\url{https://huggingface.co/datasets/ankurani/CultureScore}}

Abstract:As video generation models like Veo 3.1 and LTX-2 advance, their ability to accurately represent diverse global cultures remains a critical yet understudied frontier. Current metrics, such as VideoScore, only measure visual quality but offer no mechanism for assessing cultural faithfulness. Consequently, a model that replaces a Namaste with a handshake receives the same score as one that generates the gesture correctly. We propose CultureScore, a compositional evaluation framework that decomposes cultural faithfulness into three granular dimensions: Identity (who is represented), Context (culturally localized background), and Behavior (normative gestures and interactions). We operationalize this framework through an evaluation suite spanning 10 countries, yielding 6,174 generated videos across three state-of-the-art models. Our evaluation reveals that no current model achieves culturally faithful video generation: the best-performing model reaches only 56.8% overall CultureScore, with Behavior the most challenging dimension; no model exceeds 52.1% on behavior. Furthermore, the highest-scoring model (LTX-2) on visual quality was ranked last by native annotators, while CultureScore's Behavior dimension shows the strongest positive correlation with human cultural judgment among the automatic metrics we evaluate, underscoring that cultural faithfulness is an essential criterion for equitable video generation. Data and code are publicly available.\footnote{\url{this https URL}}

Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI)

arXiv:2606.07311 [cs.CV]

(or arXiv:2606.07311v3 [cs.CV] for this version)

https://doi.org/10.48550/arXiv.2606.07311 Focus to learn more arXiv-issued DOI via DataCite

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CultureScore: Evaluating Cultural Faithfulness in Video Generation Models

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