IEEE transactions on visualization and computer graphics最新文献_第3页

Data Playwright: Authoring Data Videos With Annotated Narration. 数据剧作家：制作带有注释旁白的数据视频。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-16 DOI: 10.1109/TVCG.2024.3477926

Leixian Shen, Haotian Li, Yun Wang, Tianqi Luo, Yuyu Luo, Huamin Qu

{"title":"Data Playwright: Authoring Data Videos With Annotated Narration.","authors":"Leixian Shen, Haotian Li, Yun Wang, Tianqi Luo, Yuyu Luo, Huamin Qu","doi":"10.1109/TVCG.2024.3477926","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3477926","url":null,"abstract":"Creating data videos that effectively narrate stories with animated visuals requires substantial effort and expertise. A promising research trend is leveraging the easy-to-use natural language (NL) interaction to automatically synthesize data video components from narrative content like text narrations, or NL commands that specify user-required designs. Nevertheless, previous research has overlooked the integration of narrative content and specific design authoring commands, leading to generated results that lack customization or fail to seamlessly fit into the narrative context. To address these issues, we introduce a novel paradigm for creating data videos, which seamlessly integrates users' authoring and narrative intents in a unified format called annotated narration, allowing users to incorporate NL commands for design authoring as inline annotations within the narration text. Informed by a formative study on users' preference for annotated narration, we develop a prototype system named Data Playwright that embodies this paradigm for effective creation of data videos. Within Data Playwright, users can write annotated narration based on uploaded visualizations. The system's interpreter automatically understands users' inputs and synthesizes data videos with narration-animation interplay, powered by large language models. Finally, users can preview and fine-tune the video. A user study demonstrated that participants can effectively create data videos with Data Playwright by effortlessly articulating their desired outcomes through annotated narration.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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FR-CSG: Fast and Reliable Modeling for Constructive Solid Geometry. FR-CSG：快速可靠的建设性实体几何建模。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-15 DOI: 10.1109/TVCG.2024.3481278

Jiaxi Chen, Zeyu Shen, Mingyang Zhao, Xiaohong Jia, Dong-Ming Yan, Wencheng Wang

{"title":"FR-CSG: Fast and Reliable Modeling for Constructive Solid Geometry.","authors":"Jiaxi Chen, Zeyu Shen, Mingyang Zhao, Xiaohong Jia, Dong-Ming Yan, Wencheng Wang","doi":"10.1109/TVCG.2024.3481278","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3481278","url":null,"abstract":"Reconstructing CSG trees from CAD models is a critical subject in reverse engineering. While there have been notable advancements in CSG reconstruction, challenges persist in capturing geometric details and achieving efficiency. Additionally, since non-axis-aligned volumetric primitives cannot maintain coplanar characteristics due to discretization errors, existing Boolean operations often lead to zero-volume surfaces and suffer from topological errors during the CSG modeling process. To address these issues, we propose a novel workflow to achieve fast CSG reconstruction and reliable forward modeling. First, we employ feature removal and model subdivision techniques to decompose models into sub-components. This significantly expedites the reconstruction by simplifying the complexity of the models. Then, we introduce a more reasonable method for primitive generation and filtering, and utilize a size-related optimization approach to reconstruct CSG trees. By re-adding features as additional nodes in the CSG trees, our method not only preserves intricate details but also ensures the conciseness, semantic integrity, and editability of the resulting CSG tree. Finally, we develop a coplanar primitive discretization method that represents primitives as large planes and extracts the original triangles after intersection. We extend the classification of triangles and incorporate a coplanar-aware Boolean tree assessment technique, allowing us to achieve manifold and watertight modeling results without zero-volume surfaces, even in extreme degenerate cases. We demonstrate the superiority of our method over state-of-the-art approaches. Moreover, the reconstructed CSG trees generated by our method contain extensive semantic information, enabling diverse model editing tasks.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142484146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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IEEE ISMAR 2024 Science & Technology Program Committee Members for Journal Papers IEEE ISMAR 2024 科学与技术项目委员会期刊论文委员

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3453150

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IEEE ISMAR 2024 Steering Committee Members IEEE ISMAR 2024 指导委员会成员

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3453149

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Message from the ISMAR 2024 Science and Technology Program Chairs and TVCG Guest Editors ISMAR 2024 科学与技术项目主席和 TVCG 特邀编辑的致辞

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3453128

Ulrich Eck;Maki Sugimoto;Misha Sra;Markus Tatzgern;Jeanine Stefanucci;Ian Williams

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IEEE ISMAR 2024 - Paper Reviewers for Journal Papers IEEE ISMAR 2024 - 期刊论文审稿人

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3453151

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2024 IEEE International Symposium on Mixed and Augmented Reality 2024 电气和电子工程师学会混合与增强现实国际研讨会

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3453109

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Charting EDA: Characterizing Interactive Visualization Use in Computational Notebooks with a Mixed-Methods Formalism. Charting EDA: Characterizing Interactive Visualization Use in Computational Notebooks with a Mixed-Methods Formalism.

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3456217

Dylan Wootton, Amy Rae Fox, Evan Peck, Arvind Satyanarayan

{"title":"Charting EDA: Characterizing Interactive Visualization Use in Computational Notebooks with a Mixed-Methods Formalism.","authors":"Dylan Wootton, Amy Rae Fox, Evan Peck, Arvind Satyanarayan","doi":"10.1109/TVCG.2024.3456217","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3456217","url":null,"abstract":"Interactive visualizations are powerful tools for Exploratory Data Analysis (EDA), but how do they affect the observations analysts make about their data? We conducted a qualitative experiment with 13 professional data scientists analyzing two datasets with Jupyter notebooks, collecting a rich dataset of interaction traces and think-aloud utterances. By qualitatively coding participant utterances, we introduce a formalism that describes EDA as a sequence of analysis states, where each state is comprised of either a representation an analyst constructs (e.g., the output of a data frame, an interactive visualization, etc.) or an observation the analyst makes (e.g., about missing data, the relationship between variables, etc.). By applying our formalism to our dataset, we identify that interactive visualizations, on average, lead to earlier and more complex insights about relationships between dataset attributes compared to static visualizations. Moreover, by calculating metrics such as revisit count and representational diversity, we uncover that some representations serve more as \"planning aids\" during EDA rather than tools strictly for hypothesis-answering. We show how these measures help identify other patterns of analysis behavior, such as the \"80-20 rule\", where a small subset of representations drove the majority of observations. Based on these fndings, we offer design guidelines for interactive exploratory analysis tooling and refect on future directions for studying the role that visualizations play in EDA.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142402491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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Message from the Editor-in-Chief and from the Associate Editor-in-Chief 主编和副主编致辞

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-10 DOI: 10.1109/TVCG.2024.3453148

Han-Wei Shen;Kiyoshi Kiyokawa

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ChartKG: A Knowledge-Graph-Based Representation for Chart Images. ChartKG：基于知识图谱的图表图像表示法。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-09 DOI: 10.1109/TVCG.2024.3476508

Zhiguang Zhou, Haoxuan Wang, Zhengqing Zhao, Fengling Zheng, Yongheng Wang, Wei Chen, Yong Wang

{"title":"ChartKG: A Knowledge-Graph-Based Representation for Chart Images.","authors":"Zhiguang Zhou, Haoxuan Wang, Zhengqing Zhao, Fengling Zheng, Yongheng Wang, Wei Chen, Yong Wang","doi":"10.1109/TVCG.2024.3476508","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3476508","url":null,"abstract":"Chart images, such as bar charts, pie charts, and line charts, are explosively produced due to the wide usage of data visualizations. Accordingly, knowledge mining from chart images is becoming increasingly important, which can benefit downstream tasks like chart retrieval and knowledge graph completion. However, existing methods for chart knowledge mining mainly focus on converting chart images into raw data and often ignore their visual encodings and semantic meanings, which can result in information loss for many downstream tasks. In this paper, we propose ChartKG, a novel knowledge graph (KG) based representation for chart images, which can model the visual elements in a chart image and semantic relations among them including visual encodings and visual insights in a unified manner.Further, we develop a general framework to convert chart images to the proposed KG-based representation. It integrates a series of image processing techniques to identify visual elements and relations, e.g., CNNs to classify charts, yolov5 and optical character recognition to parse charts, and rule-based methods to construct graphs. We present four cases to illustrate how our knowledge-graph-based representation can model the detailed visual elements and semantic relations in charts, and further demonstrate how our approach can benefit downstream applications such as semantic-aware chart retrieval and chart question answering. We also conduct quantitative evaluations to assess the two fundamental building blocks of our chart-to-KG framework, i.e., object recognition and optical character recognition. The results provide support for the usefulness and effectiveness of ChartKG.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0