@inproceedings{Li:2025:RouteFlow,
author = {Li, Duan and Guo, Xinyuan and Shu, Xinhuan and Xiao, Lanxi and Yu, Lingyun and Liu, Shixia},
title = {RouteFlow: Trajectory-Aware Animated Transitions},
year = {2025},
isbn = {9798400713941},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3706598.3714300},
doi = {10.1145/3706598.3714300},
booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
articleno = {1116},
numpages = {17},
keywords = {trajectory data, animation, edge bundling},
location = {},
series = {CHI ’25}
}

@inproceedings{10.1145/3706599.3719686,
author = {Xu, Wanfang and Zhao, Lixiang and Song, Haiwen and Song, Xinheng and Lu, Zhaolin and Liu, Yu and Chen, Min and Lim, Eng Gee and Yu, Lingyun},
title = {Mozualization: Crafting Music and Visual Representation with Multimodal AI},
year = {2025},
isbn = {9798400713958},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3706599.3719686},
doi = {10.1145/3706599.3719686},
abstract = {In this work, we introduce Mozualization, a music generation and editing tool that creates multi-style embedded music by integrating diverse inputs, such as keywords, images, and sound clips (e.g., segments from various pieces of music or even a playful cat’s meow). Our work is inspired by the ways people express their emotions—writing mood-descriptive poems or articles, creating drawings with warm or cool tones, or listening to sad or uplifting music. Building on this concept, we developed a tool that transforms these emotional expressions into a cohesive and expressive song, allowing users to seamlessly incorporate their unique preferences and inspirations. To evaluate the tool and, more importantly, gather insights for its improvement, we conducted a user study involving nine music enthusiasts. The study assessed user experience, engagement, and the impact of interacting with and listening to the generated music.},
booktitle = {Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
articleno = {407},
numpages = {7},
keywords = {Multimodal Input, Music Editing, Music Visualization},
location = {
},
series = {CHI EA '25}
}

@ARTICLE{11060027,
  author={Ye, Shuainan and Yin, Jianing and Zhou, Buwei and Tang, Tan and Yu, Lingyun and Yu, Ruohan and Jiang, Lu and Diao, Changyu and Wu, Yingcai},
  journal={Computational Visual Media}, 
  title={PuzzleSorter: Certainty-Aware Visual Restoration of Multiple Cultural Artifacts}, 
  year={2025},
  volume={11},
  number={6},
  pages={1281-1302},
  keywords={Image restoration;Visualization;Cultural differences;Assembly;Shape;Predictive models;Object recognition;Uncertainty;Interviews;Computational modeling;fragment restoration;force-directed graph;uncertainty visualization;cultural heritage},
  doi={10.26599/CVM.2025.9450468}}

@ARTICLE{10292929,
  author={Zhou, Yuxing and Yang, Weikai and Chen, Jiashu and Chen, Changjian and Shen, Zhiyang and Luo, Xiaonan and Yu, Lingyun and Liu, Shixia},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={Cluster-Aware Grid Layout}, 
  year={2024},
  volume={30},
  number={1},
  pages={240-250},
  doi={10.1109/TVCG.2023.3326934}}

@ARTICLE{10415212,
author={Tang, Junxiu and Yang, Fumeng and Wu, Jiang and Wang, Yifang and Zhou, Jiayi and Cai, Xiwen and Yu, Lingyun and Wu, Yingcai},
journal={IEEE Transactions on Visualization and Computer Graphics},
title={A Comparative Study on Fixed-order Event Sequence Visualizations: Gantt, Extended Gantt, and Stringline Charts},
year={2024},
volume={},
number={},
pages={1-15},
doi={10.1109/TVCG.2024.3358919}}

@article{yao20253dstoryline,
  title={3DStoryline: immersive visual storytelling: H. Yao et al.},
  author={Yao, Haonan and Zhao, Lixiang and Chen, Boyuan and Li, Kaiwen and Liang, Hai-Ning and Yu, Lingyun},
  journal={Journal of Visualization},
  volume={28},
  number={3},
  pages={681--697},
  year={2025},
  doi={10.1007/s12650-025-01058-5},
  publisher={Springer}
}

@article{tang2025towards,
  title={Towards better illegal chemical facility detection with hazardous chemicals transportation trajectories: J. Tang et al.},
  author={Tang, Junxiu and Ren, Huimin and Deng, Zikun and Weng, Di and Tang, Tan and Yu, Lingyun and Bao, Jie and Zheng, Yu and Wu, Yingcai},
  journal={Journal of Visualization},
  volume={28},
  number={3},
  pages={535--551},
  year={2025},
  publisher={Springer}
}

@ARTICLE{9557223,
  author={Ying, Lu and Tang, Tan and Luo, Yuzhe and Shen, Lvkeshen and Xie, Xiao and Yu, Lingyun and Wu, Yingcai},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={GlyphCreator: Towards Example-based Automatic Generation of Circular Glyphs}, 
  year={2022},
  volume={28},
  number={1},
  pages={400-410},
  keywords={Data visualization;Visualization;Layout;Deep learning;Data mining;Tools;Task analysis;Glyph-based visualization;machine learning;automatic visualization},
  doi={10.1109/TVCG.2021.3114877}}

@ARTICLE{9557224,
  author={Tang, Junxiu and Zhou, Yuhua and Tang, Tan and Weng, Di and Xie, Boyang and Yu, Lingyun and Zhang, Huaqiang and Wu, Yingcai},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={A Visualization Approach for Monitoring Order Processing in E-Commerce Warehouse}, 
  year={2022},
  volume={28},
  number={1},
  pages={857-867},
  keywords={Data visualization;Monitoring;Real-time systems;Schedules;Delays;Warehousing;Visual analytics;Streaming data;time-series data;e-commerce warehouse;order processing},
  doi={10.1109/TVCG.2021.3114878}}

@article{10.1145/3484506,
author = {Tang, Tan and Tang, Junxiu and Lai, Jiewen and Ying, Lu and Wu, Yingcai and Yu, Lingyun and Ren, Peiran},
title = {SmartShots: An Optimization Approach for Generating Videos with Data Visualizations Embedded},
year = {2022},
issue_date = {March 2022},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {12},
number = {1},
issn = {2160-6455},
url = {https://doi.org/10.1145/3484506},
doi = {10.1145/3484506},
abstract = {Videos are well-received methods for storytellers to communicate various narratives. To further engage viewers, we introduce a novel visual medium where data visualizations are embedded into videos to present data insights. However, creating such data-driven videos requires professional video editing skills, data visualization knowledge, and even design talents. To ease the difficulty, we propose an optimization method and develop SmartShots, which facilitates the automatic integration of in-video visualizations. For its development, we first collaborated with experts from different backgrounds, including information visualization, design, and video production. Our discussions led to a design space that summarizes crucial design considerations along three dimensions: visualization, embedded layout, and rhythm. Based on that, we formulated an optimization problem that aims to address two challenges: (1) embedding visualizations while considering both contextual relevance and aesthetic principles and (2) generating videos by assembling multi-media materials. We show how SmartShots solves this optimization problem and demonstrate its usage in three cases. Finally, we report the results of semi-structured interviews with experts and amateur users on the usability of SmartShots.},
journal = {ACM Trans. Interact. Intell. Syst.},
month = mar,
articleno = {4},
numpages = {21},
keywords = {Visualization, data-driven videos, optimization}
}

@ARTICLE{9222335,
  author={Tang, Tan and Li, Renzhong and Wu, Xinke and Liu, Shuhan and Knittel, Johannes and Koch, Steffen and Ertl, Thomas and Yu, Lingyun and Ren, Peiran and Wu, Yingcai},
  journal={IEEE Transactions on Visualization and Computer Graphics}, 
  title={PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning}, 
  year={2021},
  volume={27},
  number={2},
  pages={294-303},
  keywords={Layout;Visualization;Reinforcement learning;Collaboration;Task analysis;Optimization;Storyline visualization;reinforcement learning;mixed-initiative design},
  doi={10.1109/TVCG.2020.3030467}}

@INPROCEEDINGS{9331289,
  author={Tang, Junxiu and Yu, Lingyun and Tang, Tan and Shu, Xinhuan and Ying, Lu and Zhou, Yuhua and Ren, Peiran and Wu, Yingcai},
  booktitle={2020 IEEE Visualization Conference (VIS)}, 
  title={Narrative Transitions in Data Videos}, 
  year={2020},
  volume={},
  number={},
  pages={151-155},
  keywords={Visualization;Motion segmentation;Conferences;Taxonomy;Data visualization;Data mining;Videos;Human-centered computing;Visualization;Visualization theory;concepts and paradigms},
  doi={10.1109/VIS47514.2020.00037}}

@article{tang2020design,
  title={Design guidelines for augmenting short-form videos using animated data visualizations: T. Tang et al.},
  author={Tang, Tan and Tang, Junxiu and Hong, Jiayi and Yu, Lingyun and Ren, Peiran and Wu, Yingcai},
  journal={Journal of Visualization},
  volume={23},
  number={4},
  pages={707--720},
  year={2020},
  publisher={Springer}
}

@inproceedings{10.1145/3394171.3414356,
author = {Tang, Tan and Tang, Junxiu and Lai, Jiewen and Ying, Lu and Ren, Peiran and Yu, Lingyun and Wu, Yingcai},
title = {SmartShots: Enabling Automatic Generation of Videos with Data Visualizations Embedded},
year = {2020},
isbn = {9781450379885},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3394171.3414356},
doi = {10.1145/3394171.3414356},
abstract = {Videos become prevalent for storytellers to inspire viewers' interests. To further enhance narrations, visualizations are integrated into videos to present data-driven insights. However, manually crafting such data-driven videos is difficult and time-consuming. Thus, we present SmartShots, a system that facilitates the automatic integration of in-video visualizations. Specifically, we propose a computational framework that integrates non-verbal video clips, images, a melody, and a data table to create a video with data visualizations embedded. The system automatically translates the multi-media material into shots and then combines the shots into a compelling video. In addition, we develop a set of post-editing interactions to incorporate users' design knowledge and help them re-edit the automatically-generated videos.},
booktitle = {Proceedings of the 28th ACM International Conference on Multimedia},
pages = {4509–4511},
numpages = {3},
keywords = {data-driven videos, storytelling, visualization},
location = {Seattle, WA, USA},
series = {MM '20}
}

@inproceedings{3290776.3290786,
author = {Amabili, L. and Kosinka, J. and van Meersbergen, M. A. J. and van Ooijen, P. M. A. and Roerdink, J. B. T. M. and Svetachov, P. and Yu, L.},
title = {Improving provenance data interaction for visual storytelling in medical imaging data exploration},
year = {2018},
publisher = {Eurographics Association},
address = {Goslar, DEU},
abstract = {Effective collaborative work in diagnostic medical imaging is not trivial due to the large amounts of complex data involved, a (non-linear) workflow involving experts in different domains, and a lack of versatility in the current tools employed in healthcare. In this paper, we aim to introduce how the integration of visual storytelling techniques together with provenance data in the analytic systems used in medicine can compensate for these issues, by enhancing communication of results and reproducibility of findings through diagnostic provenance data. To this end, we illustrate how we can improve the interaction with provenance data displayed in a graph in order to facilitate authoring and the creation process of visual data stories.},
booktitle = {Proceedings of the Eurographics/IEEE VGTC Conference on Visualization: Short Papers},
pages = {43–47},
numpages = {5},
location = {Brno, Czech Republic},
series = {EuroVis '18}
}

@inproceedings{amabili2017visual,
  title={Visual Storytelling of Big Imaging Data},
  author={Amabili, Lorenzo and van Hage, W and van Hoesel, FHJ and Kosinka, Jiri and Yu, Lingyun and van Meersbergen, Maarten and van Ooijen, PMA and Roerdink, JBTM and Svetachov, Pjotr},
  booktitle={National eScience Symposium 2017: Science in a Digital World},
  year={2017}
}