VideoGen-Eval: Agent-based System for Video Generation Evaluation

• URL: http://arxiv.org/abs/2503.23452v2
• Date: Sun, 27 Apr 2025 02:59:54 GMT
• Title: VideoGen-Eval: Agent-based System for Video Generation Evaluation
• Authors: Yuhang Yang, Ke Fan, Shangkun Sun, Hongxiang Li, Ailing Zeng, FeiLin Han, Wei Zhai, Wei Liu, Yang Cao, Zheng-Jun Zha,
• Abstract summary: Video generation has rendered existing evaluation systems inadequate for assessing state-of-the-art models.<n>We propose VideoGen-Eval, an agent evaluation system that integrates content structuring, MLLM-based content judgment, and patch tools for temporal-dense dimensions.<n>We introduce a video generation benchmark to evaluate existing cutting-edge models and verify the effectiveness of our evaluation system.
• Score: 54.662739174367836
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid advancement of video generation has rendered existing evaluation systems inadequate for assessing state-of-the-art models, primarily due to simple prompts that cannot showcase the model's capabilities, fixed evaluation operators struggling with Out-of-Distribution (OOD) cases, and misalignment between computed metrics and human preferences. To bridge the gap, we propose VideoGen-Eval, an agent evaluation system that integrates LLM-based content structuring, MLLM-based content judgment, and patch tools designed for temporal-dense dimensions, to achieve a dynamic, flexible, and expandable video generation evaluation. Additionally, we introduce a video generation benchmark to evaluate existing cutting-edge models and verify the effectiveness of our evaluation system. It comprises 700 structured, content-rich prompts (both T2V and I2V) and over 12,000 videos generated by 20+ models, among them, 8 cutting-edge models are selected as quantitative evaluation for the agent and human. Extensive experiments validate that our proposed agent-based evaluation system demonstrates strong alignment with human preferences and reliably completes the evaluation, as well as the diversity and richness of the benchmark.

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