BizGenEval: A Systematic Benchmark for Commercial Visual Content Generation
🤖 AI Summary
This work addresses the absence of a systematic evaluation benchmark tailored to real-world commercial visual content generation in existing image synthesis models. It introduces, for the first time, a multidimensional and fine-grained assessment framework encompassing five categories of commercial documents and four core capabilities: text rendering, layout control, attribute binding, and knowledge-based reasoning. The authors conduct a large-scale evaluation of 26 state-of-the-art models using 400 carefully designed prompts and 8,000 human-verified questions. This benchmark fills a critical gap in the evaluation of professional visual generation systems and reveals substantial performance deficiencies of current models when handling complex commercial scenarios.
Application Category
📝 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, 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.
Problem
Research questions and friction points this paper is trying to address.
commercial visual content generation
benchmark
image generation
evaluation
design constraints
Innovation
Methods, ideas, or system contributions that make the work stand out.
commercial visual content generation
systematic benchmark
layout control
attribute binding
knowledge-based reasoning
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