CogCartoon: Towards Practical Story Visualization

IF 11.6 2区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Zhongyang Zhu, Jie Tang
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引用次数: 0

Abstract

The state-of-the-art methods for story visualization demonstrate a significant demand for training data and storage, as well as limited flexibility in story presentation, thereby rendering them impractical for real-world applications. We introduce CogCartoon, a practical story visualization method based on pre-trained diffusion models. To alleviate dependence on data and storage, we propose an innovative strategy of character-plugin generation that can represent a specific character as a compact 316 KB plugin by using a few training samples. To facilitate enhanced flexibility, we employ a strategy of plugin-guided and layout-guided inference, enabling users to seamlessly incorporate new characters and custom layouts into the generated image results at their convenience. We have conducted comprehensive qualitative and quantitative studies, providing compelling evidence for the superiority of CogCartoon over existing methodologies. Moreover, CogCartoon demonstrates its power in tackling challenging tasks, including long story visualization and realistic style story visualization.

Abstract Image

CogCartoon:实现实用的故事可视化
最先进的故事可视化方法需要大量的训练数据和存储空间,而且故事呈现的灵活性有限,因此在实际应用中并不实用。我们介绍了基于预训练扩散模型的实用故事可视化方法 CogCartoon。为了减轻对数据和存储的依赖,我们提出了一种创新的角色插件生成策略,只需使用少量训练样本,即可将特定角色表示为一个紧凑的 316 KB 插件。为了提高灵活性,我们采用了插件引导推理和布局引导推理的策略,使用户能够在方便的时候将新字符和自定义布局无缝纳入生成的图像结果中。我们进行了全面的定性和定量研究,为 CogCartoon 优于现有方法提供了令人信服的证据。此外,CogCartoon 还展示了其在处理长篇故事可视化和现实风格故事可视化等挑战性任务时的强大功能。
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来源期刊
International Journal of Computer Vision
International Journal of Computer Vision 工程技术-计算机:人工智能
CiteScore
29.80
自引率
2.10%
发文量
163
审稿时长
6 months
期刊介绍: The International Journal of Computer Vision (IJCV) serves as a platform for sharing new research findings in the rapidly growing field of computer vision. It publishes 12 issues annually and presents high-quality, original contributions to the science and engineering of computer vision. The journal encompasses various types of articles to cater to different research outputs. Regular articles, which span up to 25 journal pages, focus on significant technical advancements that are of broad interest to the field. These articles showcase substantial progress in computer vision. Short articles, limited to 10 pages, offer a swift publication path for novel research outcomes. They provide a quicker means for sharing new findings with the computer vision community. Survey articles, comprising up to 30 pages, offer critical evaluations of the current state of the art in computer vision or offer tutorial presentations of relevant topics. These articles provide comprehensive and insightful overviews of specific subject areas. In addition to technical articles, the journal also includes book reviews, position papers, and editorials by prominent scientific figures. These contributions serve to complement the technical content and provide valuable perspectives. The journal encourages authors to include supplementary material online, such as images, video sequences, data sets, and software. This additional material enhances the understanding and reproducibility of the published research. Overall, the International Journal of Computer Vision is a comprehensive publication that caters to researchers in this rapidly growing field. It covers a range of article types, offers additional online resources, and facilitates the dissemination of impactful research.
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