用检索与区域注入让漫画角色连贯生动
Retrieval Augmented Comic Image Generation
- 通过检索匹配文本与参考图，确保角色一致
- 区域注入将角色特征嵌入指定图像区域
- 适合漫画生成、角色一致性研究者
我们提出RaCig，一种生成连贯漫画图像序列的新系统。该系统解决两大挑战：(1) 保持角色身份与服饰在多帧中的一致性；(2) 生成多样且生动的角色动作。方法包括基于检索的角色分配模块，将文本提示中的角色与参考图像对齐，并采用区域角色注入机制，将角色特征嵌入特定图像区域。实验表明，RaCig能有效生成具有连贯角色与动态互动的吸引人漫画叙事。源代码将公开，以支持该领域的进一步研究。
原文摘要 · Abstract (English)
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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