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
The paper introduces VMetaphor-Bench, a benchmark for assessing visual metaphor generation in text-to-image models. It contains 1,500 curated metaphors across three levels and ten categories, each paired with two prompts of varying specificity. The authors evaluate 11 T2I models using a hybrid MLLM-as-judge framework that combines a large multiple-choice question set with dimension-based scoring, finding that even top proprietary models struggle with compositional structuring and cross-domain mapping.
Traditional Image Aesthetic Assessment (IAA) methods mainly rely on regressing absolute Mean Opinion Scores (MOS). However, such a paradigm overlooks the inherently dynamic nature of human aesthetic perception, which relies on subconscious comparison against implicit visual references.
arXiv:2610.18860v1 Announce Type: new
Abstract: Generating information-rich visual artifacts such as slides, posters, and dashboards as code is appealing because the outputs are structured, editable,...
By Xin Gao, Yifan Yang, Xin Li, Pan Wang, Yan Li, Muzhao Tian, Mingxi Cheng, Bei Liu, Kai Qiu, Qi Dai, Dong Chen, Yue Dong, Leandros Tassiulas, Rex Ying, Ji Li, Chong Luo
arXiv:2606. 30026v1 Announce Type: cross Abstract: Audiovisual arts encompass diverse creative disciplines, including cinema, visual arts, stage performance, and game design, where artistic meaning arises from deliberate combinations of visual, auditory, and narrative elements (e.
By Yuxuan Fan, Gyusik Seo, Jing Hao, Jaemin Cho, Mohit Bansal, Jaehong Yoon
MUSE is a new benchmark designed to evaluate large vision‑language models on artistic image understanding within situated educational contexts. It separates image annotation from question generation, offering twelve tasks that cover visual perception, semantic and affective interpretation, cultural understanding, and compositional reasoning across diverse artistic images from Singaporean, Southeast Asian, and Western traditions. The benchmark reveals significant gaps in model performance, especially in affective interpretation and compositional reasoning, and highlights common failure modes for trustworthy educational multimodal systems.
By Luyao Zhu, Xun Wei Yee, Wei Li, Mun Thye Mak, Wee Siong Ng
arXiv:2608.10706v3 Announce Type: replace
Abstract: Recent vision-language models demonstrate impressive general visual understanding, yet their art interpretation remains shallow: they describe surf...
By Shuai Wang, Wangyuan Ding, Yixian Shen, Jia-Hong Huang, Stevan Rudinac, Monika Kackovic, Nachoem Wijnberg, Marcel Worring