A Fast Text-Driven Approach for Generating Artistic Content
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2022
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| _version_ | 1866912523775639552 |
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| author | Lupascu, Marian Murdock, Ryan Mironica, Ionut Li, Yijun |
| author_facet | Lupascu, Marian Murdock, Ryan Mironica, Ionut Li, Yijun |
| contents | In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or stylization of a content image from a certain domain), our method has no such restriction. In addition, we implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure, with a boost in generation speed. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline. This module will bring additional details such as patterns specific to painters, slight brush marks, and so on. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2208_01748 |
| institution | arXiv |
| publishDate | 2022 |
| record_format | arxiv |
| spellingShingle | A Fast Text-Driven Approach for Generating Artistic Content Lupascu, Marian Murdock, Ryan Mironica, Ionut Li, Yijun Computer Vision and Pattern Recognition Multimedia In this work, we propose a complete framework that generates visual art. Unlike previous stylization methods that are not flexible with style parameters (i.e., they allow stylization with only one style image, a single stylization text or stylization of a content image from a certain domain), our method has no such restriction. In addition, we implement an improved version that can generate a wide range of results with varying degrees of detail, style and structure, with a boost in generation speed. To further enhance the results, we insert an artistic super-resolution module in the generative pipeline. This module will bring additional details such as patterns specific to painters, slight brush marks, and so on. |
| title | A Fast Text-Driven Approach for Generating Artistic Content |
| topic | Computer Vision and Pattern Recognition Multimedia |
| url | https://arxiv.org/abs/2208.01748 |