UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?

• URL: http://arxiv.org/abs/2503.09949v2
• Date: Fri, 21 Mar 2025 13:53:32 GMT
• Title: UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?
• Authors: Yuanxin Liu, Rui Zhu, Shuhuai Ren, Jiacong Wang, Haoyuan Guo, Xu Sun, Lu Jiang,
• Abstract summary: This work investigates the feasibility of using multimodal large language models (MLLMs) as a unified evaluator for AI-generated videos (AIGVs)<n> UVE-Bench collects videos generated by state-of-the-art VGMs and provides pairwise human preference annotations across 15 evaluation aspects.<n>Our results suggest that while advanced MLLMs still lag behind human evaluators, they demonstrate promising ability in unified AIGV evaluation.
• Score: 20.199060287444162
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: With the rapid growth of video generative models (VGMs), it is essential to develop reliable and comprehensive automatic metrics for AI-generated videos (AIGVs). Existing methods either use off-the-shelf models optimized for other tasks or rely on human assessment data to train specialized evaluators. These approaches are constrained to specific evaluation aspects and are difficult to scale with the increasing demands for finer-grained and more comprehensive evaluations. To address this issue, this work investigates the feasibility of using multimodal large language models (MLLMs) as a unified evaluator for AIGVs, leveraging their strong visual perception and language understanding capabilities. To evaluate the performance of automatic metrics in unified AIGV evaluation, we introduce a benchmark called UVE-Bench. UVE-Bench collects videos generated by state-of-the-art VGMs and provides pairwise human preference annotations across 15 evaluation aspects. Using UVE-Bench, we extensively evaluate 16 MLLMs. Our empirical results suggest that while advanced MLLMs (e.g., Qwen2VL-72B and InternVL2.5-78B) still lag behind human evaluators, they demonstrate promising ability in unified AIGV evaluation, significantly surpassing existing specialized evaluation methods. Additionally, we conduct an in-depth analysis of key design choices that impact the performance of MLLM-driven evaluators, offering valuable insights for future research on AIGV evaluation. The code is available at https://github.com/bytedance/UVE.

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