arXiv:2606. 31711v1 Announce Type: new Abstract: Faithfulness -- how precisely a generated image aligns with its prompt -- is increasingly central to the real-world utility of text-to-image (T2I) models.
By Yuanhao Ban, Tong Xie, Sohyun An, Yunqi Hong, Evan Frick, I-Hung Hsu, Wei-Lin Chiang, Ion Stoica, Cho-Jui Hsieh
arXiv:2603. 13312v2 Announce Type: replace-cross Abstract: Interior design is a requirements-to-visual-plan generation process that must simultaneously satisfy verifiable spatial feasibility and comparative aesthetic preferences.
By Yuxuan Yang, Xiaotong Mao, Jingyao Wang, Fuchun Sun
arXiv:2602. 15278v2 Announce Type: replace-cross Abstract: The web is littered with images, once created for human consumption and now increasingly interpreted by agents using vision-language models (VLMs).
By Manuel Cherep, Pranav M R, Pattie Maes, Nikhil Singh
AesCanvas is a new dataset and benchmark that evaluates image aesthetic models on two fronts: CritiqueCanvas, which contains 519,136 instruction–response pairs for long‑form, multi‑dimensional critique across photography, painting, and virtual imagery, and ContextCanvas, which offers 301 expert‑reviewed use scenarios to assess contextual aesthetic suitability. The benchmark tests closed‑source, open‑weight general, and aesthetic‑specific multimodal large language models, revealing that models excel at critique generation but lag in context‑sensitive judgment. The study shows that aesthetic specialization does not reliably transfer to contextual suitability and highlights the need for culturally situated, evidence‑grounded suitability as a distinct objective for aesthetic modeling.
By Xuanwei Hu, Haoyu Dong, Kejun Wu, Tianyi Liu, Jianjun Gao
The study audits four image aesthetic scorers—LAION-Aesthetics, PickScore, ImageReward, and HPSv2—using pixel‑level interventions on skin tone and body type in both synthetic and real images. It finds that most scorers exhibit a fidelity preference: unaltered images receive the highest scores, while perturbations in either direction are penalized in an inverted‑U pattern, and this effect is largely independent of the skin operator. Synthetic‑only audits are misleading, as the apparent preference for darker skin in synthetic faces reverses or weakens when evaluated on real faces, and cross‑scorer results vary widely, underscoring the need for real‑data, within‑image causal isolation to accurately assess demographic bias.
By Mingyang Xu
arXiv:2606. 05478v1 Announce Type: cross Abstract: Diffusion Models (DM) have revolutionized text-driven generation by enabling the synthesis of high-quality, photorealistic visual content from user prompts.
By Joong Ho Kim, Keith G. Mills