The study evaluates whether multimodal large language models (MLLMs) can produce open‑ended aesthetic critiques comparable to humans. Eight open‑weight MLLMs (7 B–397 B) and GPT‑5.5 were tested on 1,227 r/photocritique posts under various prompts, revealing that reference‑based similarity metrics often misrepresent model performance, while shorter critiques and image‑omission had limited impact. Human judges and annotators found the models’ critiques largely different from human ones, noting that models tend to be overly comprehensive and repetitive rather than selective and specific.
By Sajjad Ghiasvand, Maryam Amirizaniani, Haniyeh Ehsani Oskouie, Mahnoosh Alizadeh, Ramtin Pedarsani
Despite advances in photorealism, semantic fidelity, and aesthetics, text-to-image generation and image editing remain understudied in a human-centered dimension: whether their outputs feel psychologi...
arXiv:2610.27516v1 Announce Type: cross
Abstract: Despite advances in photorealism, semantic fidelity, and aesthetics, text-to-image generation and image editing remain understudied in a human-center...
By Junjie Xu, Yuzhe Yang, Rui Yang, Luwei Xiao, Wenxin Wu, Xingjiao Wu, Liang He
PortraitAes: Intent-Conditioned Structured Portrait Aesthetic Assessment introduces a new benchmark, PortraitAes-Bench, with 11,000 images and a detailed rubric covering nine photographic intents, six first-level dimensions, and 22 secondary criteria. The model predicts aesthetic scores conditioned on photographic intent, using multi-task supervision and Gaussian score calibration to improve comparability. On the benchmark, PortraitAes achieves high Pearson (0.924) and Spearman (0.934) correlations, outperforming general-purpose MLLMs and specialized baselines.
By Junzhou Xie, Haozhong Xiong, Xunyun Tian, Kaile Du, Tianchen Yu, Qiang Li, Wei Liu, Jiaming Liu, Ruihua Huang, Yang Shi, Guangcan Liu
arXiv:2607. 04147v1 Announce Type: cross Abstract: Automated fine-grained perception of calligraphy styles--a task vital to cultural heritage preservation--remains a critical challenge for Large Vision-Language Models (LVLMs), largely constrained by existing datasets that suffer from modal mixture and flattened labels.
By Yinsheng Yao, Yan Liu, Chen Ye
The paper introduces VMetaphor-Bench, a benchmark for evaluating visual metaphor generation in text-to-image models, comprising 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. It proposes a hybrid evaluation framework using a multiple-choice question protocol and dimension-based scoring to assess metaphorical fidelity. Experiments on 11 T2I models show that even top proprietary models struggle with compositional structuring and cross-domain mapping, underscoring the need for further research in this area.
By Chuer Chen, Zichen Wang, Yi He, Zhengxi Yu, Nan Cao