Advertisement Synthesis Network for Automatic Advertisement Image Synthesis
Image advertising is widely used by companies to advertise their products and increase awareness of their brands. With the constant development of image generation techniques, automatic compositing of advertisement images has also been widely studied. However, the existing algorithms cannot synthesi...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Journal of Antennas and Propagation |
| Online Access: | http://dx.doi.org/10.1155/2024/8030907 |
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| _version_ | 1841524304401924096 |
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| author | Qin Wu Peizi Zhou |
| author_facet | Qin Wu Peizi Zhou |
| author_sort | Qin Wu |
| collection | DOAJ |
| description | Image advertising is widely used by companies to advertise their products and increase awareness of their brands. With the constant development of image generation techniques, automatic compositing of advertisement images has also been widely studied. However, the existing algorithms cannot synthesise consistent-looking advertisement images for a given product. The key challenge is to stitch a given product into a scene that matches the style of the product while maintaining a consistent-looking. To solve this problem, this paper proposes a new two-stage automatic advertisement image generation model, called Advertisement Synthesis Network (ASNet), which explores a two-stage generation framework to synthesise consistent-looking product advertisement images. Specifically, ASNet first generates a preliminary target product scene using Pre-Synthesis and then extracts scene features using Pseudo-Target Object Encoder (PTOE) and true target features using Real Target Object Encoder (RTOE), respectively. Finally, we inject the acquired features into the pretrained diffusion model and reconstruct them in the preliminary generated target goods scene. Extensive experiments have shown that the method achieves better results in all three performance metrics related to the quality of the synthesised image compared to other methods. In addition, we have done a simple and preliminary study on the effect of synthetic advertisement images on real consumers’ purchase intention and brand perception. The results of the study show that the advertisement images synthesised by the model proposed in this paper have a positive impact on consumer purchase intention and brand perception. |
| format | Article |
| id | doaj-art-d2bf3b6accf74366a2bfccd43351a031 |
| institution | Kabale University |
| issn | 1687-5877 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Antennas and Propagation |
| spelling | doaj-art-d2bf3b6accf74366a2bfccd43351a0312025-02-03T07:23:24ZengWileyInternational Journal of Antennas and Propagation1687-58772024-01-01202410.1155/2024/8030907Advertisement Synthesis Network for Automatic Advertisement Image SynthesisQin Wu0Peizi Zhou1College of Humanities and LawTropical Agriculture and Forestry SchoolImage advertising is widely used by companies to advertise their products and increase awareness of their brands. With the constant development of image generation techniques, automatic compositing of advertisement images has also been widely studied. However, the existing algorithms cannot synthesise consistent-looking advertisement images for a given product. The key challenge is to stitch a given product into a scene that matches the style of the product while maintaining a consistent-looking. To solve this problem, this paper proposes a new two-stage automatic advertisement image generation model, called Advertisement Synthesis Network (ASNet), which explores a two-stage generation framework to synthesise consistent-looking product advertisement images. Specifically, ASNet first generates a preliminary target product scene using Pre-Synthesis and then extracts scene features using Pseudo-Target Object Encoder (PTOE) and true target features using Real Target Object Encoder (RTOE), respectively. Finally, we inject the acquired features into the pretrained diffusion model and reconstruct them in the preliminary generated target goods scene. Extensive experiments have shown that the method achieves better results in all three performance metrics related to the quality of the synthesised image compared to other methods. In addition, we have done a simple and preliminary study on the effect of synthetic advertisement images on real consumers’ purchase intention and brand perception. The results of the study show that the advertisement images synthesised by the model proposed in this paper have a positive impact on consumer purchase intention and brand perception.http://dx.doi.org/10.1155/2024/8030907 |
| spellingShingle | Qin Wu Peizi Zhou Advertisement Synthesis Network for Automatic Advertisement Image Synthesis International Journal of Antennas and Propagation |
| title | Advertisement Synthesis Network for Automatic Advertisement Image Synthesis |
| title_full | Advertisement Synthesis Network for Automatic Advertisement Image Synthesis |
| title_fullStr | Advertisement Synthesis Network for Automatic Advertisement Image Synthesis |
| title_full_unstemmed | Advertisement Synthesis Network for Automatic Advertisement Image Synthesis |
| title_short | Advertisement Synthesis Network for Automatic Advertisement Image Synthesis |
| title_sort | advertisement synthesis network for automatic advertisement image synthesis |
| url | http://dx.doi.org/10.1155/2024/8030907 |
| work_keys_str_mv | AT qinwu advertisementsynthesisnetworkforautomaticadvertisementimagesynthesis AT peizizhou advertisementsynthesisnetworkforautomaticadvertisementimagesynthesis |