Retrieval Augmented Comic Image Generation

• URL: http://arxiv.org/abs/2506.12517v1
• Date: Sat, 14 Jun 2025 14:18:47 GMT
• Title: Retrieval Augmented Comic Image Generation
• Authors: Yunhao Shui, Xuekuan Wang, Feng Qiu, Yuqiu Huang, Jinzhu Li, Haoyu Zheng, Jinru Han, Zhuo Zeng, Pengpeng Zhang, Jiarui Han, Keqiang Sun,
• Abstract summary: We present RaCig, a novel system for generating comic-style image sequences with consistent characters and expressive gestures.<n>RaCig addresses two key challenges: maintaining character identity and costume consistency across frames, and producing diverse and vivid character gestures.
• Score: 2.8594383542895385
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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