VideoAesBench: Benchmarking the Video Aesthetics Perception Capabilities of Large Multimodal Models

• URL: http://arxiv.org/abs/2601.21915v2
• Date: Sun, 01 Feb 2026 10:19:54 GMT
• Title: VideoAesBench: Benchmarking the Video Aesthetics Perception Capabilities of Large Multimodal Models
• Authors: Yunhao Li, Sijing Wu, Zhilin Gao, Zicheng Zhang, Qi Jia, Huiyu Duan, Xiongkuo Min, Guangtao Zhai,
• Abstract summary: We introduce VideoAesBench, a benchmark for evaluating large multimodal models' understanding of video aesthetic quality.<n>VideoAesBench has diverse content including 1,804 videos from multiple video sources including user-generated (UGC), AI-generated (AIGC), compressed, robotic-generated (RGC), and game videos.<n>Our findings show that current LMMs only contain basic video aesthetics perception ability, their performance remains incomplete and imprecise.
• Score: 99.14832826329739
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Large multimodal models (LMMs) have demonstrated outstanding capabilities in various visual perception tasks, which has in turn made the evaluation of LMMs significant. However, the capability of video aesthetic quality assessment, which is a fundamental ability for human, remains underexplored for LMMs. To address this, we introduce VideoAesBench, a comprehensive benchmark for evaluating LMMs' understanding of video aesthetic quality. VideoAesBench has several significant characteristics: (1) Diverse content including 1,804 videos from multiple video sources including user-generated (UGC), AI-generated (AIGC), compressed, robotic-generated (RGC), and game videos. (2) Multiple question formats containing traditional single-choice questions, multi-choice questions, True or False questions, and a novel open-ended questions for video aesthetics description. (3) Holistic video aesthetics dimensions including visual form related questions from 5 aspects, visual style related questions from 4 aspects, and visual affectiveness questions from 3 aspects. Based on VideoAesBench, we benchmark 23 open-source and commercial large multimodal models. Our findings show that current LMMs only contain basic video aesthetics perception ability, their performance remains incomplete and imprecise. We hope our VideoAesBench can be served as a strong testbed and offer insights for explainable video aesthetics assessment. The data will be released on https://github.com/michaelliyunhao/VideoAesBench

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