CVMar 18, 2025

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

arXiv:2503.14064v16 citationsh-index: 4
Originality Incremental advance
AI Analysis

This addresses the problem of fragmented and dataset-dependent evaluation for researchers and developers in AI-generated video synthesis, though it is incremental as it builds on existing methodologies.

The authors tackled the lack of standardized evaluation metrics for AI-generated videos by introducing AIGVE-Tool, a unified framework with a modular pipeline and a large-scale benchmark dataset, which demonstrated effectiveness in providing reliable evaluations across nine quality dimensions.

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.

Code Implementations1 repo
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