Manga Generation via Layout-controllable Diffusion
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2024
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866909441599733760 |
|---|---|
| author | Chen, Siyu Li, Dengjie Bao, Zenghao Zhou, Yao Tan, Lingfeng Zhong, Yujie Zhao, Zheng |
| author_facet | Chen, Siyu Li, Dengjie Bao, Zenghao Zhou, Yao Tan, Lingfeng Zhong, Yujie Zhao, Zheng |
| contents | Generating comics through text is widely studied. However, there are few studies on generating multi-panel Manga (Japanese comics) solely based on plain text. Japanese manga contains multiple panels on a single page, with characteristics such as coherence in storytelling, reasonable and diverse page layouts, consistency in characters, and semantic correspondence between panel drawings and panel scripts. Therefore, generating manga poses a significant challenge. This paper presents the manga generation task and constructs the Manga109Story dataset for studying manga generation solely from plain text. Additionally, we propose MangaDiffusion to facilitate the intra-panel and inter-panel information interaction during the manga generation process. The results show that our method particularly ensures the number of panels, reasonable and diverse page layouts. Based on our approach, there is potential to converting a large amount of textual stories into more engaging manga readings, leading to significant application prospects. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_19303 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Manga Generation via Layout-controllable Diffusion Chen, Siyu Li, Dengjie Bao, Zenghao Zhou, Yao Tan, Lingfeng Zhong, Yujie Zhao, Zheng Computer Vision and Pattern Recognition Generating comics through text is widely studied. However, there are few studies on generating multi-panel Manga (Japanese comics) solely based on plain text. Japanese manga contains multiple panels on a single page, with characteristics such as coherence in storytelling, reasonable and diverse page layouts, consistency in characters, and semantic correspondence between panel drawings and panel scripts. Therefore, generating manga poses a significant challenge. This paper presents the manga generation task and constructs the Manga109Story dataset for studying manga generation solely from plain text. Additionally, we propose MangaDiffusion to facilitate the intra-panel and inter-panel information interaction during the manga generation process. The results show that our method particularly ensures the number of panels, reasonable and diverse page layouts. Based on our approach, there is potential to converting a large amount of textual stories into more engaging manga readings, leading to significant application prospects. |
| title | Manga Generation via Layout-controllable Diffusion |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2412.19303 |