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Generative adversarial text-to-image generation with style image constraint

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Abstract

Most text-to-image generation works focus on the semantic consistency and neglect the style of the generated image. In this paper, a novel text-to-image generation method is proposed to generate image with style image constraint. In order to provide more comprehensive information by mining long–short-range information dependencies, the multi-group attention module is introduced to capture the multi-scale dependency information in the semantic feature. The adaptive multi-scale attention normalization is adopted to pay the multi-scale style feature attention in the style fusion process. The style information related to semantic feature is filtered out by the style feature attention. This selected style information is transferred to the generated results by aligning the mean and variance of the semantic feature and the style feature. Experiments conducted on common datasets show the validity of the proposed approach.

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Acknowledgements

This work is partially supported by the National Natural Science Foundation of China (Nos. 62076153, 62176144), the major fundamental research project of Shandong, China (No. ZR2019ZD03), and the Taishan Scholar Project of Shandong, China (No. ts20190924).

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Authors and Affiliations

  1. School of Information Science and Engineering, Shandong Normal University, Jinan, China

    Zekang Wang, Li Liu, Huaxiang Zhang, Dongmei Liu & Yu Song

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  1. Zekang Wang
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  2. Li Liu
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  3. Huaxiang Zhang
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  4. Dongmei Liu
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  5. Yu Song
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ZW wrote the main manuscript text and prepared figures. All authors reviewed the manuscript.

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Correspondence to Li Liu.

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Communicated by B. Bao.

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Wang, Z., Liu, L., Zhang, H. et al. Generative adversarial text-to-image generation with style image constraint. Multimedia Systems 29, 3291–3303 (2023). https://doi.org/10.1007/s00530-023-01160-4

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