DeepArt: A Benchmark to Advance Fidelity Research in AI-Generated Content
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 contribution...
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Main Authors | , , , |
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Format | Journal Article |
Language | English |
Published |
16.12.2023
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Subjects | |
Online Access | Get full text |
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Summary: | 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}. |
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DOI: | 10.48550/arxiv.2312.10407 |