Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models

• URL: http://arxiv.org/abs/2509.26165v3
• Date: Wed, 15 Oct 2025 16:53:06 GMT
• Title: Human-MME: A Holistic Evaluation Benchmark for Human-Centric Multimodal Large Language Models
• Authors: Yuansen Liu, Haiming Tang, Jinlong Peng, Jiangning Zhang, Xiaozhong Ji, Qingdong He, Wenbin Wu, Donghao Luo, Zhenye Gan, Junwei Zhu, Yunhang Shen, Chaoyou Fu, Chengjie Wang, Xiaobin Hu, Shuicheng Yan,
• Abstract summary: Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks.<n>Human-MME is a curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric scene understanding.<n>Our benchmark extends the single-target understanding to the multi-person and multi-image mutual understanding.
• Score: 118.44328586173556
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs) have demonstrated significant advances in visual understanding tasks. However, their capacity to comprehend human-centric scenes has rarely been explored, primarily due to the absence of comprehensive evaluation benchmarks that take into account both the human-oriented granular level and higher-dimensional causal reasoning ability. Such high-quality evaluation benchmarks face tough obstacles, given the physical complexity of the human body and the difficulty of annotating granular structures. In this paper, we propose Human-MME, a curated benchmark designed to provide a more holistic evaluation of MLLMs in human-centric scene understanding. Compared with other existing benchmarks, our work provides three key features: 1. Diversity in human scene, spanning 4 primary visual domains with 15 secondary domains and 43 sub-fields to ensure broad scenario coverage. 2. Progressive and diverse evaluation dimensions, evaluating the human-based activities progressively from the human-oriented granular perception to the higher-dimensional reasoning, consisting of eight dimensions with 19,945 real-world image question pairs and an evaluation suite. 3. High-quality annotations with rich data paradigms, constructing the automated annotation pipeline and human-annotation platform, supporting rigorous manual labeling to facilitate precise and reliable model assessment. Our benchmark extends the single-target understanding to the multi-person and multi-image mutual understanding by constructing the choice, short-answer, grounding, ranking and judgment question components, and complex questions of their combination. The extensive experiments on 17 state-of-the-art MLLMs effectively expose the limitations and guide future MLLMs research toward better human-centric image understanding. All data and code are available at https://github.com/Yuan-Hou/Human-MME.

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