MVU-Eval: Towards Multi-Video Understanding Evaluation for Multimodal LLMs

Tianhao Peng, Haochen Wang, Yuanxing Zhang, Zekun Moore Wang, Zili Wang, Ge Zhang, Jian Yang, Shihao Li, Yanghai Wang, Xintao Wang, Houyi Li, Wei Ji, Pengfei Wan, Wenhao Huang, Zhaoxiang Zhang, Jiaheng Liu

Keywords: Video Understanding, Multimodal Large Language Models, Reasoning

Abstract:

The advent of Multimodal Large Language Models (MLLMs) has expanded AI capabilities to visual modalities, yet existing evaluation benchmarks remain limited to single-video understanding, overlooking the critical need for multi-video understanding in real-world scenarios (e.g., sports analytics and autonomous driving). To address this significant gap, we introduce **MVU-Eval**, the first comprehensive benchmark for evaluating **M**ulti-**V**ideo **U**nderstanding for MLLMs. Specifically, our MVU-Eval mainly assesses eight core competencies through 1,824 meticulously curated question-answer pairs spanning 4,959 videos from diverse domains, addressing both fundamental perception tasks and high-order reasoning tasks. These capabilities are rigorously aligned with real-world applications such as multi-sensor synthesis in autonomous systems and cross-angle sports analytics. Through extensive evaluation of state-of-the-art open-source and closed-source models, we reveal significant performance discrepancies and limitations in current MLLMs' ability to perform understanding across multiple videos.
The benchmark will be made publicly available to foster future research.

Dataset URL: https://huggingface.co/datasets/MVU-Eval-Team/MVU-Eval-Data

Code URL: https://huggingface.co/datasets/MVU-Eval-Team/MVU-Eval-Data

Primary Area: Datasets & Benchmarks for applications in language modeling and vision language modeling

Submission Number: 677