Retrieval Augmented Comic Image Generation

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.

@article{shui2025_2506.12517,
  title={ Retrieval Augmented Comic Image Generation },
  author={ Yunhao Shui and Xuekuan Wang and Feng Qiu and Yuqiu Huang and Jinzhu Li and Haoyu Zheng and Jinru Han and Zhuo Zeng and Pengpeng Zhang and Jiarui Han and Keqiang Sun },
  journal={arXiv preprint arXiv:2506.12517},
  year={ 2025 }
}