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arXiv · 2503.14064

AIGVE-Tool: AI-Generated Video Evaluation Toolkit with Multifaceted Benchmark

Xinhao Xiang·Xiao Liu·Zizhong Li·Zhuosheng Liu·Jiawei Zhang
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Abstract

The rapid advancement in AI-generated video synthesis has led to a growth demand for standardized and effective evaluation metrics. Existing metrics lack a unified framework for systematically categorizing methodologies, limiting a holistic understanding of the evaluation landscape. Additionally, fragmented implementations and the absence of standardized interfaces lead to redundant processing overhead. Furthermore, many prior approaches are constrained by dataset-specific dependencies, limiting their applicability across diverse video domains. To address these challenges, we introduce AIGVE-Tool (AI-Generated Video Evaluation Toolkit), a unified framework that provides a structured and extensible evaluation pipeline for a comprehensive AI-generated video evaluation. Organized within a novel five-category taxonomy, AIGVE-Tool integrates multiple evaluation methodologies while allowing flexible customization through a modular configuration system. Additionally, we propose AIGVE-Bench, a large-scale benchmark dataset created with five SOTA video generation models based on hand-crafted instructions and prompts. This dataset systematically evaluates various video generation models across nine critical quality dimensions. Extensive experiments demonstrate the effectiveness of AIGVE-Tool in providing standardized and reliable evaluation results, highlighting specific strengths and limitations of current models and facilitating the advancements of next-generation AI-generated video techniques.

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Xinhao Xiang, Xiao Liu, Zizhong Li, Zhuosheng Liu, Jiawei Zhang. 2025-03-18. AIGVE-Tool: AI-Generated Video Evaluation Toolkit with Multifaceted Benchmark. https://arxiv.org/abs/2503.14064

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