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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2503.16795 |
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| _version_ | 1866908276766015488 |
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| author | Hu, Yihan Peng, Jianing Lin, Yiheng Liu, Ting Qu, Xiaochao Liu, Luoqi Zhao, Yao Wei, Yunchao |
| author_facet | Hu, Yihan Peng, Jianing Lin, Yiheng Liu, Ting Qu, Xiaochao Liu, Luoqi Zhao, Yao Wei, Yunchao |
| contents | This paper presents a novel approach to improving text-guided image editing using diffusion-based models. Text-guided image editing task poses key challenge of precisly locate and edit the target semantic, and previous methods fall shorts in this aspect. Our method introduces a Precise Semantic Localization strategy that leverages visual and textual self-attention to enhance the cross-attention map, which can serve as a regional cues to improve editing performance. Then we propose a Dual-Level Control mechanism for incorporating regional cues at both feature and latent levels, offering fine-grained control for more precise edits. To fully compare our methods with other DiT-based approaches, we construct the RW-800 benchmark, featuring high resolution images, long descriptive texts, real-world images, and a new text editing task. Experimental results on the popular PIE-Bench and RW-800 benchmarks demonstrate the superior performance of our approach in preserving background and providing accurate edits. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_16795 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DCEdit: Dual-Level Controlled Image Editing via Precisely Localized Semantics Hu, Yihan Peng, Jianing Lin, Yiheng Liu, Ting Qu, Xiaochao Liu, Luoqi Zhao, Yao Wei, Yunchao Computer Vision and Pattern Recognition This paper presents a novel approach to improving text-guided image editing using diffusion-based models. Text-guided image editing task poses key challenge of precisly locate and edit the target semantic, and previous methods fall shorts in this aspect. Our method introduces a Precise Semantic Localization strategy that leverages visual and textual self-attention to enhance the cross-attention map, which can serve as a regional cues to improve editing performance. Then we propose a Dual-Level Control mechanism for incorporating regional cues at both feature and latent levels, offering fine-grained control for more precise edits. To fully compare our methods with other DiT-based approaches, we construct the RW-800 benchmark, featuring high resolution images, long descriptive texts, real-world images, and a new text editing task. Experimental results on the popular PIE-Bench and RW-800 benchmarks demonstrate the superior performance of our approach in preserving background and providing accurate edits. |
| title | DCEdit: Dual-Level Controlled Image Editing via Precisely Localized Semantics |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2503.16795 |