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

TL;DR

ServImage introduces a comprehensive benchmark with datasets, scoring, and a payment prediction model to evaluate the commercial viability of image generation and editing models in real-world design projects.

Contribution

It provides the first large-scale, economically grounded benchmark for assessing image models' performance in commercial design contexts.

Findings

The dataset includes over 1,000 paid commercial tasks and 2,000 designer deliverables.

The scoring system combines requirements, visual quality, and commercial necessity.

The payment prediction model achieves 82% accuracy in predicting human payment decisions.

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 \href{https://github.com/FengxianJi/ServImage}{Github.}