Abstract Art Interpretation Using ControlNet
Fuente:
arXiv
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| Main Authors: | , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866914922044063744 |
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| author | Srivastava, Rishabh Roy, Addrish |
| author_facet | Srivastava, Rishabh Roy, Addrish |
| contents | Our study delves into the fusion of abstract art interpretation and text-to-image synthesis, addressing the challenge of achieving precise spatial control over image composition solely through textual prompts. Leveraging the capabilities of ControlNet, we empower users with finer control over the synthesis process, enabling enhanced manipulation of synthesized imagery. Inspired by the minimalist forms found in abstract artworks, we introduce a novel condition crafted from geometric primitives such as triangles. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_13287 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Abstract Art Interpretation Using ControlNet Srivastava, Rishabh Roy, Addrish Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning Our study delves into the fusion of abstract art interpretation and text-to-image synthesis, addressing the challenge of achieving precise spatial control over image composition solely through textual prompts. Leveraging the capabilities of ControlNet, we empower users with finer control over the synthesis process, enabling enhanced manipulation of synthesized imagery. Inspired by the minimalist forms found in abstract artworks, we introduce a novel condition crafted from geometric primitives such as triangles. |
| title | Abstract Art Interpretation Using ControlNet |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2408.13287 |