Recent advances in multimodal large language models (MLLMs) have demonstrated impressive performance on existing low-level vision benchmarks, which primarily focus on generic images. However, their capabilities to perceive and assess portrait images, a domain characterized by distinct structural and perceptual properties, remain largely underexplored. To this end, we introduce Q-Bench-Portrait, the first holistic benchmark specifically designed for portrait image quality perception, comprising 2,765 image-question-answer triplets and featuring (1) diverse portrait image sources, including natural, synthetic distortion, AI-generated, artistic, and computer graphics images; (2) comprehensive quality dimensions, covering technical distortions, AIGC-specific distortions, and aesthetics; and (3) a range of question formats, including single-choice, multiple-choice, true/false, and open-ended questions, at both global and local levels. Based on Q-Bench-Portrait, we evaluate 20 open-source and 5 closed-source MLLMs, revealing that although current models demonstrate some competence in portrait image perception, their performance remains limited and imprecise, with a clear gap relative to human judgments. We hope that the proposed benchmark will foster further research into enhancing the portrait image perception capabilities of both general-purpose and domain-specific MLLMs.

翻译：近年来，多模态大语言模型（MLLMs）在现有主要关注通用图像的低层视觉基准测试中展现出令人瞩目的性能。然而，对于具有独特结构和感知特性的人像图像领域，其感知与评估能力在很大程度上仍未得到充分探索。为此，我们提出了Q-Bench-Portrait，这是首个专门为人像图像质量感知设计的综合性基准测试。该基准包含2,765个图像-问题-答案三元组，并具备以下特点：（1）多样的人像图像来源，包括自然图像、合成失真图像、AI生成图像、艺术图像和计算机图形图像；（2）全面的质量维度，涵盖技术失真、AIGC特定失真以及美学评价；（3）多种问题形式，包括单选题、多选题、判断题和开放式问题，且覆盖全局和局部两个层面。基于Q-Bench-Portrait，我们对20个开源和5个闭源MLLMs进行了评估。结果表明，尽管当前模型在人像图像感知方面表现出一定的能力，但其性能仍然有限且不够精确，与人类判断存在明显差距。我们希望所提出的基准测试能够推动进一步研究，以增强通用及领域专用MLLMs的人像图像感知能力。