HumanVideo-MME: Benchmarking MLLMs for Human-Centric Video Understanding

yuxuan cai, Jiangning Zhang, Zhenye Gan, Qingdong He, Xiaobin Hu, Junwei Zhu, Yabiao Wang, Chengjie Wang, Zhucun Xue, Chaoyou Fu, Xinwei He, Xiang Bai

Keywords: MLLM Benchmark, Video Understanding

Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks involving both images and videos. However, their capacity to comprehend human-centric video data remains underexplored, primarily due to the absence of comprehensive and high-quality evaluation benchmarks. Existing human-centric benchmarks predominantly emphasize video generation quality and action recognition, while overlooking essential perceptual and cognitive abilities required in human-centered scenarios. Furthermore, they are often limited by single-question paradigms and overly simplistic evaluation metrics. To address above limitations, we propose a modern HV-MMBench, a rigorously curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric video understanding. Compared to existing human-centric video benchmarks, our work offers the following key features: (1) Diverse evaluation dimensions: HV-MMBench encompasses 15 tasks, ranging from basic attribute perception (e.g., age estimation, emotion recognition) to advanced cognitive reasoning (e.g., social relationship prediction, intention prediction), enabling comprehensive assessment of model capabilities; (2) Varied data types: The benchmark includes multiple-choice, fill-in-blank, true/false, and open-ended question formats, combined with diverse evaluation metrics, to more accurately and robustly reflect model performance; (3) Multi-domain video coverage: The benchmark spans 50 distinct visual scenarios, enabling comprehensive evaluation across fine-grained scene variations; (4) Temporal coverage: The benchmark covers videos from short-term (10 seconds) to long-term (up to 30min) durations, supporting systematic analysis of models’ temporal reasoning abilities across diverse contextual lengths. We evaluate several advanced open-source MLLMs on the HV-MMBench. While models excel in closed-form tasks, their performance drops sharply in open-ended generation, revealing a reliance on shallow patterns over genuine reasoning. In contrast, cloze and open-ended formats better expose reasoning challenges in human behavior understanding. By spanning diverse tasks and paradigms, HV-MMBench systematically reveals these limitations and facilitates the MLLM development.

Primary Area: datasets and benchmarks

Submission Number: 2636