Image Generation Evaluation

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12 papers in the last four weeks, up 140% on the four weeks before. 0.1% of all new papers.

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GlazyBench: A Benchmark for Ceramic Glaze Property Prediction and Image Generation
May 7, 2026Ziyu Zhai, Siyou Li, Juexi Shao +1Conditional Image Generation Materials Property Prediction
Queen Mary University of London
The First Controllable Bokeh Rendering Challenge at NTIRE 2026
May 6, 2026Tim Seizinger, Florin-Alexandru Vasluianu, Jeffrey Chen +23Benchmark Design Neural Rendering
NTIRE 2026 challenge organizers
StableI2I: Spotting Unintended Changes in Image-to-Image Transition
May 6, 2026Jiayang Li, Shuo Cao, Xiaohui Li +6VLM Evaluation Image Editing Evaluation
Peking University · Shanghai Artificial Intelligence Laboratory · University of Science and Technology of China +1
Assessing the Geographic Diversity of AI's Platial Representations in Image Generation
Apr 28, 2026Zilong Liu, Krzysztof Janowicz, Mina KarimiImage Generation Evaluation T2I Generation Evaluation
Department of Geography and Regional Research, University of Vienna, Austria
ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services
Apr 27, 2026Fengxian Ji, Jingpu Yang, Zirui Song +5Image Editing Evaluation Image Generation Evaluation
MBZUAI, United Arab Emirates · Institute of Automation, Chinese Academy of Sciences, China
Evaluation of image simulation open source solutions for simulation of synthetic images in lunar environment
Apr 24, 2026Jai G Singla, Hinal B Patel, Nitant DubeImage Generation Evaluation Synthetic Data Evaluation
Space Applications centre Indian Space Research Organization Ahmedabad, India
StyleID: A Perception-Aware Dataset and Metric for Stylization-Agnostic Facial Identity Recognition
Apr 23, 2026Kwan Yun, Changmin Lee, Ayeong Jeong +3Metric Learning Face Recognition
KAIST, South Korea
Evaluating Generative Models via One-Dimensional Code Distributions
Mar 9, 2026Zexi Jia, Pengcheng Luo, Yijia Zhong +2Image Quality Assessment Image Generation Evaluation
WeChat AI, Tencent Inc., China · School of Intelligence Science and Technology, Peking University · College of Computer Science and Artificial Intelligence, Fudan University
Consistent Distributed Ranking of Generative Models via Kernel Distances
Oct 18, 2023Zixiao Wang, Farzan Farnia, Zhenghao Lin +2Kernel Methods Image Generation Evaluation
MedGEN-Bench: A Contextually Entangled Benchmark for Open-ended Multimodal Medical Generation
Date pendingJunjie Yang, Yuhao Yan, Gang Wu +12Healthcare Multimodal Generation
South China University of Technology · Sun Yat-sen University · Hangzhou Dianzi University +10
Persistent Identity Preservation in Generative Image Models: A Benchmark and Evaluation System
Date pendingMengwei Ren, Xuaner Zhang, Zhihao XiaSubject-Driven Image Generation Identity-Preserving Image Generation
Phota Labs Research

May 7, 2026, cs.AIJ/K move · Enter open · S save

GlazyBench: A Benchmark for Ceramic Glaze Property Prediction and Image Generation

Ziyu Zhai, Siyou Li, Juexi Shao, Juntao Yu

Queen Mary University of London

Conditional Image Generation Materials Property Prediction Image Generation Evaluation

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Developing ceramic glazes is a costly, time-consuming process of trial and error due to complex chemistry, placing a significant burden on independent artists. While recent advances in multimodal AI offer a modern solution, the field lacks the large-scale datasets required to train these models. We propose GlazyBench, the first dataset for AI-assisted glaze design. Comprising 23,148 real glaze formulations, GlazyBench supports two primary tasks: predicting post-firing surface properties, such as color and transparency, from raw materials, and generating accurate visual representations of the glaze based on these properties. We establish comprehensive baselines for property prediction using traditional machine learning and large language models, alongside image generation benchmarks using deep generative and large multimodal models. Our experiments demonstrate promising yet challenging results. GlazyBench pioneers a new research direction in AI-assisted material design, providing a standardized benchmark for systematic evaluation.

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