Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866912066662563840 |
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| author | Lupascu, Marian Mironica, Ionut Stupariu, Mihai-Sorin |
| author_facet | Lupascu, Marian Mironica, Ionut Stupariu, Mihai-Sorin |
| contents | Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the background image. Another method involves placing design elements in non-salient and contrasting regions, but this isn't always effective, especially with patterned backgrounds. To address these challenges, we propose a generative approach using a diffusion model. This method ensures the altered regions beneath design assets exhibit low saliency while enhancing contrast, thereby improving the visibility of the design asset. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_07211 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations Lupascu, Marian Mironica, Ionut Stupariu, Mihai-Sorin Computer Vision and Pattern Recognition Graphics Human-Computer Interaction Machine Learning Creating visually appealing composites requires optimizing both text and background for compatibility. Previous methods have focused on simple design strategies, such as changing text color or adding background shapes for contrast. These approaches are often destructive, altering text color or partially obstructing the background image. Another method involves placing design elements in non-salient and contrasting regions, but this isn't always effective, especially with patterned backgrounds. To address these challenges, we propose a generative approach using a diffusion model. This method ensures the altered regions beneath design assets exhibit low saliency while enhancing contrast, thereby improving the visibility of the design asset. |
| title | Neural Contrast: Leveraging Generative Editing for Graphic Design Recommendations |
| topic | Computer Vision and Pattern Recognition Graphics Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2410.07211 |