Create3DHistory: Creating and Customizing Historical Timelines with AI-Generated Content
In the digital era, traditional methods of historical learning are undergoing a transition, as there is a growing demand for intuitive and visually engaging approaches. Vi-sualization, particularly timeline-based visualization, is critical in bridging the gap between extensive and complex historical events and history learners due to its intuitive features. Despite the advances in existing timeline visualization tools, they often face challenges: visualization creators spend much time in the materials preparation stage, especially for designers unfamiliar with the history; the lack of flexible interaction limits users from designing customized representations, which decreases the user's interest. To address these issues, we have developed Create3DHistory, a system that leverages the advanced Artificial Intelligence Generated Content and visualization technologies, enabling users to easily generate historical events content and create 3D and 2D historical event timeline representation. Users only need to input the name of a historical event, and the system will automatically generate corresponding content and deploy it onto the timeline representation. Moreover, users can edit the generated content or upload self-prepared multimedia materials for customized event visualization. The user study has demonstrated that Create3DHistory can effectively assist users in customizing personalized historical event timelines. Overall, the proposed tool simplifies the timeline creation process using AIGC technology, providing an overview and detailed information on-demand functions for various tasks.
Authors
- Yejuan Xie (ORCID: https://orcid.org/0009-0003-6187-6663)
- Peiyu Hu
- Chengtao Ji (ORCID: https://orcid.org/0000-0001-5733-6881)
- Rongrong Chen (ORCID: https://orcid.org/0000-0001-8287-3478)
- Yushi Li (ORCID: https://orcid.org/0000-0003-3600-6394)
- Yunzhe Wang (ORCID: https://orcid.org/0000-0002-1755-6430)
Institutions
- Beijing Normal-Hong Kong Baptist University (CN)
- Suzhou University of Science and Technology (CN)
- Xi’an Jiaotong-Liverpool University (CN)
Publication Details
- Published
- 2025-05-05
- DOI
- https://doi.org/10.1109/cscwd64889.2025.11033575
- Primary Topic
- Computational and Text Analysis Methods
- Type
- article
- Field-Weighted Citation Impact
- 0.00