arXiv:2505. 16915v3 Announce Type: replace-cross Abstract: While recent Text-to-Image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, they struggle with the long, detailed prompts required for professional applications.
By Qirui Jiao, Daoyuan Chen, Yilun Huang, Xika Lin, Ying Shen, Yaliang Li
arXiv:2606. 28406v1 Announce Type: new Abstract: Text-to-image and multimodal generative models are increasingly used to produce scientific figures such as mechanism diagrams, experimental-design schematics, conceptual frameworks, and graphical abstracts.
By Davie Chen
arXiv:2609.37576v1 Announce Type: new
Abstract: With the rapid advancement of text-to-image (T2I) generation, robust evaluation becomes critical yet challenging, as traditional metrics fail to captur...
By Yu Zhao, Jiarui Wang, Huiyu Duan, Ye Zhao, Jutao Tang, Juntong Wang, Guangtao Zhai, Xiongkuo Min
Multimodal Language Models as Text-to-Image Model Evaluators presents MT2IE, a framework where a multimodal large language model generates evaluation prompts and scores images, achieving higher correlation with human judgment than prior metrics. MT2IE recovers official T2I model rankings using only 20 prompts—far fewer than traditional benchmarks—and adapts prompts to each model’s performance, maintaining informative scoring ranges. The approach demonstrates that dynamic, interactive evaluation can replace static benchmarks as T2I models improve.
By Jiahui Chen, Candace Ross, Reyhane Askari-Hemmat, Koustuv Sinha, Melissa Hall, Amy Zhang, Michal Drozdzal, Adriana Romero-Soriano
arXiv:2609.24228v1 Announce Type: new
Abstract: Text-to-image (T2I) models are typically evaluated for bias using slot-based templates such as ``a photo of a [profession]''. Such templates probe only...
By Yue Dai, Ziyang Liu, Marc Cheong, Caren Han
The paper introduces a benchmark and evaluation system for measuring how well generative image models preserve the identity of a subject across generation, editing, restoration, and multi‑subject scenarios. It compares three paradigms—input context, trainable subject‑specific parameters, and a persistent identity layer—showing that persistent identity consistently improves fidelity while keeping image quality and instruction adherence high. The study finds that identity preservation remains a distinct limitation of current foundation models, especially under iterative edits, small scales, severe degradation, and multi‑subject composition.
By Mengwei Ren, Xuaner Zhang, Zhihao Xia