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| Main Authors: | , , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2504.14108 |
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| _version_ | 1866908558810939392 |
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| author | Yu, Zhenyu Idris, Mohd Yamani Idna Wang, Hua Wang, Pei Qureshi, Rizwan Raza, Shaina Chadha, Aman Xiang, Yong Chen, Zhixiang |
| author_facet | Yu, Zhenyu Idris, Mohd Yamani Idna Wang, Hua Wang, Pei Qureshi, Rizwan Raza, Shaina Chadha, Aman Xiang, Yong Chen, Zhixiang |
| contents | We present DanceText, a training-free framework for multilingual text editing in images, designed to support complex geometric transformations and achieve seamless foreground-background integration. While diffusion-based generative models have shown promise in text-guided image synthesis, they often lack controllability and fail to preserve layout consistency under non-trivial manipulations such as rotation, translation, scaling, and warping. To address these limitations, DanceText introduces a layered editing strategy that separates text from the background, allowing geometric transformations to be performed in a modular and controllable manner. A depth-aware module is further proposed to align appearance and perspective between the transformed text and the reconstructed background, enhancing photorealism and spatial consistency. Importantly, DanceText adopts a fully training-free design by integrating pretrained modules, allowing flexible deployment without task-specific fine-tuning. Extensive experiments on the AnyWord-3M benchmark demonstrate that our method achieves superior performance in visual quality, especially under large-scale and complex transformation scenarios. Code is avaible at https://github.com/YuZhenyuLindy/DanceText.git. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_14108 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images Yu, Zhenyu Idris, Mohd Yamani Idna Wang, Hua Wang, Pei Qureshi, Rizwan Raza, Shaina Chadha, Aman Xiang, Yong Chen, Zhixiang Computer Vision and Pattern Recognition We present DanceText, a training-free framework for multilingual text editing in images, designed to support complex geometric transformations and achieve seamless foreground-background integration. While diffusion-based generative models have shown promise in text-guided image synthesis, they often lack controllability and fail to preserve layout consistency under non-trivial manipulations such as rotation, translation, scaling, and warping. To address these limitations, DanceText introduces a layered editing strategy that separates text from the background, allowing geometric transformations to be performed in a modular and controllable manner. A depth-aware module is further proposed to align appearance and perspective between the transformed text and the reconstructed background, enhancing photorealism and spatial consistency. Importantly, DanceText adopts a fully training-free design by integrating pretrained modules, allowing flexible deployment without task-specific fine-tuning. Extensive experiments on the AnyWord-3M benchmark demonstrate that our method achieves superior performance in visual quality, especially under large-scale and complex transformation scenarios. Code is avaible at https://github.com/YuZhenyuLindy/DanceText.git. |
| title | DanceText: A Training-Free Layered Framework for Controllable Multilingual Text Transformation in Images |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2504.14108 |