UVE: Are MLLMs Unified Evaluators for AI-Generated Videos?
Authors: name: Yuanxin Liu, name: Rui Zhu, name: Shuhuai Ren, name: Jiacong Wang, name: Haoyuan Guo, name: Xu Sun, name: Lu Jiang
Organization: ByteDance Seed

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.