CharCom: Composable Identity Control for Multi-Character Story Illustration
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866914165972533248 |
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| author | Wang, Zhongsheng Lin, Ming Lin, Zhedong Shakib, Yaser Liu, Qian Liu, Jiamou |
| author_facet | Wang, Zhongsheng Lin, Ming Lin, Zhedong Shakib, Yaser Liu, Qian Liu, Jiamou |
| contents | Ensuring character identity consistency across varying prompts remains a fundamental limitation in diffusion-based text-to-image generation. We propose CharCom, a modular and parameter-efficient framework that achieves character-consistent story illustration through composable LoRA adapters, enabling efficient per-character customization without retraining the base model. Built on a frozen diffusion backbone, CharCom dynamically composes adapters at inference using prompt-aware control. Experiments on multi-scene narratives demonstrate that CharCom significantly enhances character fidelity, semantic alignment, and temporal coherence. It remains robust in crowded scenes and enables scalable multi-character generation with minimal overhead, making it well-suited for real-world applications such as story illustration and animation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_10135 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | CharCom: Composable Identity Control for Multi-Character Story Illustration Wang, Zhongsheng Lin, Ming Lin, Zhedong Shakib, Yaser Liu, Qian Liu, Jiamou Artificial Intelligence Ensuring character identity consistency across varying prompts remains a fundamental limitation in diffusion-based text-to-image generation. We propose CharCom, a modular and parameter-efficient framework that achieves character-consistent story illustration through composable LoRA adapters, enabling efficient per-character customization without retraining the base model. Built on a frozen diffusion backbone, CharCom dynamically composes adapters at inference using prompt-aware control. Experiments on multi-scene narratives demonstrate that CharCom significantly enhances character fidelity, semantic alignment, and temporal coherence. It remains robust in crowded scenes and enables scalable multi-character generation with minimal overhead, making it well-suited for real-world applications such as story illustration and animation. |
| title | CharCom: Composable Identity Control for Multi-Character Story Illustration |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2510.10135 |