Retrieval Augmented Comic Image Generation
Fuente:
arXiv
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| Auteurs principaux: | , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866913893901664256 |
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| author | Shui, Yunhao Wang, Xuekuan Qiu, Feng Huang, Yuqiu Li, Jinzhu Zheng, Haoyu Han, Jinru Zeng, Zhuo Zhang, Pengpeng Han, Jiarui Sun, Keqiang |
| author_facet | Shui, Yunhao Wang, Xuekuan Qiu, Feng Huang, Yuqiu Li, Jinzhu Zheng, Haoyu Han, Jinru Zeng, Zhuo Zhang, Pengpeng Han, Jiarui Sun, Keqiang |
| contents | We present RaCig, a novel system for generating comic-style image sequences with consistent characters and expressive gestures. RaCig addresses two key challenges: (1) maintaining character identity and costume consistency across frames, and (2) producing diverse and vivid character gestures. Our approach integrates a retrieval-based character assignment module, which aligns characters in textual prompts with reference images, and a regional character injection mechanism that embeds character features into specified image regions. Experimental results demonstrate that RaCig effectively generates engaging comic narratives with coherent characters and dynamic interactions. The source code will be publicly available to support further research in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_12517 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Retrieval Augmented Comic Image Generation Shui, Yunhao Wang, Xuekuan Qiu, Feng Huang, Yuqiu Li, Jinzhu Zheng, Haoyu Han, Jinru Zeng, Zhuo Zhang, Pengpeng Han, Jiarui Sun, Keqiang Computer Vision and Pattern Recognition We present RaCig, a novel system for generating comic-style image sequences with consistent characters and expressive gestures. RaCig addresses two key challenges: (1) maintaining character identity and costume consistency across frames, and (2) producing diverse and vivid character gestures. Our approach integrates a retrieval-based character assignment module, which aligns characters in textual prompts with reference images, and a regional character injection mechanism that embeds character features into specified image regions. Experimental results demonstrate that RaCig effectively generates engaging comic narratives with coherent characters and dynamic interactions. The source code will be publicly available to support further research in this area. |
| title | Retrieval Augmented Comic Image Generation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.12517 |