Low-Resolution Editing is All You Need for High-Resolution Editing
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866908929540227072 |
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| author | Lee, Junsung Lee, Hyunsoo Lee, Yong Jae Han, Bohyung |
| author_facet | Lee, Junsung Lee, Hyunsoo Lee, Yong Jae Han, Bohyung |
| contents | High-resolution content creation is rapidly emerging as a central challenge in both the vision and graphics communities. Images serve as the most fundamental modality for visual expression, and content generation that aligns with the user intent requires effective, controllable high-resolution image manipulation mechanisms. However, existing approaches remain limited to low-resolution settings, typically supporting only up to 1K resolution. In this work, we introduce the task of high-resolution image editing and propose a test-time optimization framework to address it. Our method performs patch-wise optimization on high-resolution source images, followed by a fine-grained detail transfer module and a novel synchronization strategy to maintain consistency across patches. Extensive experiments show that our method produces high-quality edits, facilitating high-resolution content creation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19945 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Low-Resolution Editing is All You Need for High-Resolution Editing Lee, Junsung Lee, Hyunsoo Lee, Yong Jae Han, Bohyung Computer Vision and Pattern Recognition High-resolution content creation is rapidly emerging as a central challenge in both the vision and graphics communities. Images serve as the most fundamental modality for visual expression, and content generation that aligns with the user intent requires effective, controllable high-resolution image manipulation mechanisms. However, existing approaches remain limited to low-resolution settings, typically supporting only up to 1K resolution. In this work, we introduce the task of high-resolution image editing and propose a test-time optimization framework to address it. Our method performs patch-wise optimization on high-resolution source images, followed by a fine-grained detail transfer module and a novel synchronization strategy to maintain consistency across patches. Extensive experiments show that our method produces high-quality edits, facilitating high-resolution content creation. |
| title | Low-Resolution Editing is All You Need for High-Resolution Editing |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.19945 |