VF-Eval: Evaluating Multimodal LLMs for Generating Feedback on AIGC
  Videos
Authors: name: Tingyu Song, name: Tongyan Hu, name: Guo Gan, name: Yilun Zhao

MLLMs have been widely studied for video question answering recently.
However, most existing assessments focus on natural videos, overlooking
synthetic videos, such as AI-generated content (AIGC). Meanwhile, some works in
video generation rely on MLLMs to evaluate the quality of generated videos, but
the capabilities of MLLMs on interpreting AIGC videos remain largely
underexplored. To address this, we propose a new benchmark, VF-Eval, which
introduces four tasks-coherence validation, error awareness, error type
detection, and reasoning evaluation-to comprehensively evaluate the abilities
of MLLMs on AIGC videos. We evaluate 13 frontier MLLMs on VF-Eval and find that
even the best-performing model, GPT-4.1, struggles to achieve consistently good
performance across all tasks. This highlights the challenging nature of our
benchmark. Additionally, to investigate the practical applications of VF-Eval
in improving video generation, we conduct an experiment, RePrompt,
demonstrating that aligning MLLMs more closely with human feedback can benefit
video generation.