Retrieval Augmented Comic Image Generation
Yunhao Shui, Xuekuan Wang, Feng Qiu, Yuqiu Huang, Jinzhu Li, Haoyu Zheng, Jinru Han, Zhuo Zeng, Pengpeng Zhang, Jiarui Han, Keqiang Sun

TL;DR
RaCig is a new system that generates comic-style image sequences with consistent characters and expressive gestures by combining retrieval-based character alignment and regional feature embedding.
Contribution
It introduces a retrieval-based character assignment and regional injection mechanism for coherent and expressive comic image generation.
Findings
Effective generation of comic narratives with consistent characters
Maintains character identity and costume across frames
Produces diverse, vivid character gestures
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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Taxonomy
TopicsHuman Motion and Animation · Artificial Intelligence in Games · Handwritten Text Recognition Techniques
