BizGenEval: A Systematic Benchmark for Commercial Visual Content Generation
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, we introduce BizGenEval, a systematic benchmark for commercial visual content generation. The benchmark spans five representative document types: slides, charts, webpages, posters, and scientific figures, and evaluates four key capability dimensions: text rendering, layout control, attribute binding, and knowledge-based reasoning, forming 20 diverse evaluation tasks. BizGenEval contains 400 carefully curated prompts and 8000 human-verified checklist questions to rigorously assess whether generated images satisfy complex visual and semantic constraints. We conduct large-scale benchmarking on 26 popular image generation systems, including state-of-the-art commercial APIs and leading open-source models. The results reveal substantial capability gaps between current generative models and the requirements of professional visual content creation. We hope BizGenEval serves as a standardized benchmark for real-world commercial visual content generation.
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Yan Li
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Zezi Zeng
69ccf47b1f6782218d973c18
Ziwei Zhou
69ccf47b1f6782218d973c19
Xin Gao
69ccf47b1f6782218d973c1a
Muzhao Tian
69ccf47b1f6782218d973c1b
Yifan Yang
claimed_verified
2026-04-02T08:17:11.250Z
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Yif Yang
Yif29
user
Yif29
69ccf47b1f6782218d973c1c
Mingxi Cheng
69ccf47b1f6782218d973c1d
Qi Dai
69ccf47b1f6782218d973c1e
Yuqing Yang
69ccf47b1f6782218d973c1f
Lili Qiu
69ccf47b1f6782218d973c20
Zhendong Wang
69ccf47b1f6782218d973c21
Zhengyuan Yang
69ccf47b1f6782218d973c22
Xue Yang
69ccf47b1f6782218d973c23
Lijuan Wang
69ccf47b1f6782218d973c24
Ji Li
69ccf47b1f6782218d973c25
Chong Luo
BizGenEval: A Systematic Benchmark for Commercial Visual Content Generation
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, we introduce BizGenEval, a systematic benchmark for commercial visual content generation. The benchmark spans five representative document types: slides, charts, webpages, posters, and scientific figures, and evaluates four key capability dimensions: text rendering, layout control, attribute binding, and knowledge-based reasoning, forming 20 diverse evaluation tasks. BizGenEval contains 400 carefully curated prompts and 8000 human-verified checklist questions to rigorously assess whether generated images satisfy complex visual and semantic constraints. We conduct large-scale benchmarking on 26 popular image generation systems, including state-of-the-art commercial APIs and leading open-source models. The results reveal substantial capability gaps between current generative models and the requirements of professional visual content creation. We hope BizGenEval serves as a standardized benchmark for real-world commercial visual content generation.
A new benchmark called BizGenEval is introduced to evaluate image generation models on commercial visual content creation tasks across multiple document types and capability dimensions.
BizGenEval image generation models commercial visual content creation document types evaluation tasks capability dimensions text rendering layout control attribute binding knowledge-based reasoning