Are Video Generation Models Geographically Fair? An Attraction-Centric Evaluation of Global Visual Knowledge

• URL: http://arxiv.org/abs/2601.18698v1
• Date: Mon, 26 Jan 2026 17:14:57 GMT
• Title: Are Video Generation Models Geographically Fair? An Attraction-Centric Evaluation of Global Visual Knowledge
• Authors: Xiao Liu, Jiawei Zhang,
• Abstract summary: We investigate the geo-equity and geographically grounded visual knowledge of text-to-video models through an attraction-centric evaluation.<n>We introduce Geo-Attraction Landmark Probing (GAP), a systematic framework for assessing how faithfully models synthesize tourist attractions from diverse regions.<n>GAP integrates complementary metrics that disentangle overall video quality from attraction-specific knowledge, including global structural alignment, fine-grained keypoint-based alignment, and vision-language model judgments.
• Score: 10.25143146695869
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent advances in text-to-video generation have produced visually compelling results, yet it remains unclear whether these models encode geographically equitable visual knowledge. In this work, we investigate the geo-equity and geographically grounded visual knowledge of text-to-video models through an attraction-centric evaluation. We introduce Geo-Attraction Landmark Probing (GAP), a systematic framework for assessing how faithfully models synthesize tourist attractions from diverse regions, and construct GEOATTRACTION-500, a benchmark of 500 globally distributed attractions spanning varied regions and popularity levels. GAP integrates complementary metrics that disentangle overall video quality from attraction-specific knowledge, including global structural alignment, fine-grained keypoint-based alignment, and vision-language model judgments, all validated against human evaluation. Applying GAP to the state-of-the-art text-to-video model Sora 2, we find that, contrary to common assumptions of strong geographic bias, the model exhibits a relatively uniform level of geographically grounded visual knowledge across regions, development levels, and cultural groupings, with only weak dependence on attraction popularity. These results suggest that current text-to-video models express global visual knowledge more evenly than expected, highlighting both their promise for globally deployed applications and the need for continued evaluation as such systems evolve.

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