@inproceedings{rani-etal-2026-culturescore,
title = "{C}ulture{S}core: Evaluating Cultural Faithfulness in Video Generation Models",
author = "Rani, Anku and
Dai, Wei and
Nayak, Shravan and
Maes, Patricia and
Kalayeh, Mahdi M. and
Liang, Paul Pu",
editor = "Lee, Hung-Yi and
Dev, Sunipa and
Li, Jessy and
Elliott, Desmond",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EMNLP} 2026",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-emnlp.2166/",
pages = "46330--46363",
ISBN = "979-8-89176-462-0",
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.[{\ensuremath{<}}https://huggingface.co/datasets/ankurani/CultureScore{\ensuremath{>}}]"
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<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.[\ensuremath<https://huggingface.co/datasets/ankurani/CultureScore\ensuremath>]</abstract>
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%0 Conference Proceedings
%T CultureScore: Evaluating Cultural Faithfulness in Video Generation Models
%A Rani, Anku
%A Dai, Wei
%A Nayak, Shravan
%A Maes, Patricia
%A Kalayeh, Mahdi M.
%A Liang, Paul Pu
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F rani-etal-2026-culturescore
%X 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.[\ensuremath<https://huggingface.co/datasets/ankurani/CultureScore\ensuremath>]
%U https://aclanthology.org/2026.findings-emnlp.2166/
%P 46330-46363
Markdown (Informal)
[CultureScore: Evaluating Cultural Faithfulness in Video Generation Models](https://aclanthology.org/2026.findings-emnlp.2166/) (Rani et al., Findings 2026)
ACL