FingER: Content Aware Fine-grained Evaluation with Reasoning for AI-Generated Videos

• URL: http://arxiv.org/abs/2504.10358v1
• Date: Mon, 14 Apr 2025 16:07:16 GMT
• Title: FingER: Content Aware Fine-grained Evaluation with Reasoning for AI-Generated Videos
• Authors: Rui Chen, Lei Sun, Jing Tang, Geng Li, Xiangxiang Chu,
• Abstract summary: We propose a novel entity-level reasoning evaluation framework, $textbfF$ine-grained $textbfE$ntity-level questions.<n>Our model surpasses existing methods by a relative margin of $11.8%$ on GenAI-Bench and $5.5%$ on MonetBench with only 3.3k training videos.
• Score: 18.3012265316413
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recent advances in video generation have posed great challenges in the assessment of AI-generated content, particularly with the emergence of increasingly sophisticated models. The various inconsistencies and defects observed in such videos are inherently complex, making overall scoring notoriously difficult. In this paper, we emphasize the critical importance of integrating fine-grained reasoning into video evaluation, and we propose $\textbf{F}$ing$\textbf{ER}$, a novel entity-level reasoning evaluation framework that first automatically generates $\textbf{F}$ine-grained $\textbf{E}$ntity-level questions, and then answers those questions by a $\textbf{R}$easoning model with scores, which can be subsequently weighted summed to an overall score for different applications. Specifically, we leverage LLMs to derive entity-level questions across five distinct perspectives, which (i) often focus on some specific entities of the content, thereby making answering or scoring much easier by MLLMs, and (ii) are more interpretable. Then we construct a FingER dataset, consisting of approximately 3.3k videos and corresponding 60k fine-grained QA annotations, each with detailed reasons. Based on that, we further investigate various training protocols to best incentivize the reasoning capability of MLLMs for correct answer prediction. Extensive experiments demonstrate that a reasoning model trained using Group Relative Policy Optimization (GRPO) with a cold-start strategy achieves the best performance. Notably, our model surpasses existing methods by a relative margin of $11.8\%$ on GenAI-Bench and $5.5\%$ on MonetBench with only 3.3k training videos, which is at most one-tenth of the training samples utilized by other methods. Our code and dataset will be released soon.

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