Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2405.16330 |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917883422965760 |
|---|---|
| author | Singh, Silky Jandial, Surgan Shahid, Simra Java, Abhinav |
| author_facet | Singh, Silky Jandial, Surgan Shahid, Simra Java, Abhinav |
| contents | Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization. In this work, we evaluate the text-conditioned image editing and style transfer techniques on their fine-grained understanding of user prompts for precise "local" style transfer. We find that current methods fail to accomplish localized style transfers effectively, either failing to localize style transfer to certain regions in the image, or distorting the content and structure of the input image. To this end, we develop an end-to-end pipeline for "local" style transfer tailored to align with users' intent. Further, we substantiate the effectiveness of our approach through quantitative and qualitative analysis. The project code is available at: https://github.com/silky1708/local-style-transfer. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_16330 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | LEAST: "Local" text-conditioned image style transfer Singh, Silky Jandial, Surgan Shahid, Simra Java, Abhinav Computer Vision and Pattern Recognition Text-conditioned style transfer enables users to communicate their desired artistic styles through text descriptions, offering a new and expressive means of achieving stylization. In this work, we evaluate the text-conditioned image editing and style transfer techniques on their fine-grained understanding of user prompts for precise "local" style transfer. We find that current methods fail to accomplish localized style transfers effectively, either failing to localize style transfer to certain regions in the image, or distorting the content and structure of the input image. To this end, we develop an end-to-end pipeline for "local" style transfer tailored to align with users' intent. Further, we substantiate the effectiveness of our approach through quantitative and qualitative analysis. The project code is available at: https://github.com/silky1708/local-style-transfer. |
| title | LEAST: "Local" text-conditioned image style transfer |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.16330 |