ServImage: An Image Generation and Editing Benchmark from Real-world Commercial Imaging Services

ACL ARR 2026 January Submission7506 Authors

Keywords: ServImage, image generation, image editing, paid commercial design tasks, economic value, payment prediction

Abstract:

Recent image generation and editing models demonstrate robust adherence to instructions and high visual quality on academic benchmarks.
However, their performance on paid, real-world design projects remains uncertain.
We introduce \textbf{ServImage}, a benchmark that explicitly correlates model outputs with economic value in commercial design projects.
ServImage consists of
(i) \textbf{\textit{ServImageBench}}: a dataset of 1.07k paid commercial design tasks and 2.05k designer deliverables totaling over \$295k, covering portrait, product, and digital content, along with 33k candidate images and 33k human annotations.
(ii) \textbf{\textit{ServImageScore}}: an integrated scoring system that combines three quality dimensions: baseline requirements fulfilment, visual execution quality, and commercial necessity satisfaction. These three dimensions are designed to characterize the factors that drive human payment decisions and indicate whether an image is commercially acceptable.
(iii) \textbf{\textit{ServImageModel}}: under this scoring system, we propose a payment prediction model trained on the human-annotated candidate images, achieving 82.00\% accuracy in predicting human payment decisions and producing calibrated payment probabilities.
ServImage provides a comprehensive foundation for assessing the commercial viability of image generation models and offers a scalable resource for future research on economically grounded vision systems
\faGithub~\href{https://github.com/mllmagent-jpg/ServImage/}{Github.}

Paper Type: Long

Research Area: Resources and Evaluation

Research Area Keywords: benchmarking, dataset creation, evaluation methodologies, metrics

Contribution Types: NLP engineering experiment, Data resources, Data analysis

Languages Studied: English

Submission Number: 7506