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SLVMEval: Synthetic Meta Evaluation Benchmark for Text-to-Long Video Generation
来源日期（仅到年份） 2026
来源记录日期 2026
雷达观测 2026-11-05 01:31
摘要
This paper proposes the synthetic long-video meta-evaluation (SLVMEval), a benchmark to perform meta-evaluations of text-to-video (T2V) evaluation systems. The proposed SLVMEval benchmark focuses on assessing these systems on videos of up to 10,486 s (approximately 3 h). The benchmark targets a fundamental requirement, i.e., whether the systems can accurately assess video quality in settings that are easy for humans to assess. We adopt a pairwise comparison-based meta-evaluation framework. Building on dense video-captioning datasets, we synthetically degrade source videos to create controlled "high-quality versus low-quality" pairs across 10 distinct aspects. Then, we employ crowdsourcing to filter and retain only those pairs in which the degradation is clearly perceptible, thereby establishing an effective final testbed. Using this testbed, we assess the reliability of existing evaluation systems in ranking these pairs. Experimental results demonstrate that human evaluators can identify the better long video with 84.7%--96.8% accuracy, and in nine of the 10 aspects, the accuracy of these systems falls short of the human assessment, which reveals weaknesses in text-to-long video evaluation.
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记录 #1 · 2026 · 最新观测版本
SLVMEval: Synthetic Meta Evaluation Benchmark for Text-to-Long Video Generation
This paper proposes the synthetic long-video meta-evaluation (SLVMEval), a benchmark to perform meta-evaluations of text-to-video (T2V) evaluation systems. The proposed SLVMEval benchmark focuses on assessing these systems on videos of up to 10,486 s (approximately 3 h). The benchmark targets a fundamental requirement, i.e., whether the systems can accurately assess video quality in settings that are easy for humans to assess. We adopt a pairwise comparison-based meta-evaluation framework. Building on dense video-captioning datasets, we synthetically degrade source videos to create controlled "high-quality versus low-quality" pairs across 10 distinct aspects. Then, we employ crowdsourcing to filter and retain only those pairs in which the degradation is clearly perceptible, thereby establishing an effective final testbed. Using this testbed, we assess the reliability of existing evaluation systems in ranking these pairs. Experimental results demonstrate that human evaluators can identify the better long video with 84.7%--96.8% accuracy, and in nine of the 10 aspects, the accuracy of these systems falls short of the human assessment, which reveals weaknesses in text-to-long video evaluation.
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