This paper explores the image synthesis capabilities of GPT-4, a leading multi-modal large language model. We establish a benchmark for evaluating the fidelity of texture features in images generated by GPT-4, comprising manually painted pictures and their AI-generated counterparts. The contributions of this study are threefold: First, we provide an in-depth analysis of the fidelity of image synthesis features based on GPT-4, marking the first such study on this state-of-the-art model. Second, the quantitative and qualitative experiments fully reveals the limitations of the GPT-4 model in image synthesis. Third, we have compiled a unique benchmark of manual drawings and corresponding GPT-4-generated images, introducing a new task to advance fidelity research in AI-generated content (AIGC). The dataset is available at: \url{https://github.com/rickwang28574/DeepArt}.
@article{arxiv.2312.10407,
title = {DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content},
author = {Wentao Wang and Xuanyao Huang and Tianyang Wang and Swalpa Kumar Roy},
journal= {arXiv preprint arXiv:2312.10407},
year = {2023}
}
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This is the second version of this work, and new contributors join and the modification content is greatly increased