Overview
Research Area: Computer Vision arXiv: 2603.25732v1 Posted: 2026-03-26 (auto-collected 2026-03-28)
Abstract
Recent advances in image generation models have expanded their applications beyond aesthetic imagery toward practical visual content creation. However, existing benchmarks mainly focus on natural image synthesis and fail to systematically evaluate models under the structured and multi-constraint requirements of real-world commercial design tasks.
In this work, the authors introduce BizGenEval, a systematic benchmark for commercial visual content generation.
Key Features
- Five document types: slides, charts, webpages, posters, and scientific figures
- Four capability dimensions: text rendering, layout control, attribute binding, and knowledge-based reasoning
- 20 diverse evaluation tasks formed by combining the above
- 400 carefully curated prompts and 8,000 human-verified checklist questions to rigorously evaluate whether generated images satisfy complex visual and semantic constraints
Evaluation
The authors conducted a large-scale benchmark of 26 popular image generation systems, including state-of-the-art commercial APIs and leading open-source models. The results reveal significant capability gaps between current generative models and the requirements of professional visual content creation.
The authors hope BizGenEval can serve as a standardized benchmark for real-world commercial visual content generation.
Authors
Yan Li, Zezi Zeng, Ziwei Zhou, Xin Gao, Muzhao Tian, Yifan Yang, Mingxi Cheng, Qi Dai, Yuqing Yang, Lili Qiu, Zhendong Wang, Zhengyuan Yang, Xue Yang, Lijuan Wang, Ji Li, Chong Luo