IEEE transactions on visualization and computer graphics最新文献_第5页

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

引用次数: 0

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

引用次数: 0

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":"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 findings, we offer design guidelines for interactive exploratory analysis tooling and reflect on future directions for studying the role that visualizations play in EDA.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"31 1","pages":"1191-1201"},"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}

引用次数: 0

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

MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field. MM-NeRF：多模态引导的神经辐射场三维多类型转移。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-08 DOI: 10.1109/TVCG.2024.3476331

Zijiang Yang, Zhongwei Qiu, Chang Xu, Dongmei Fu

{"title":"MM-NeRF: Multimodal-Guided 3D Multi-Style Transfer of Neural Radiance Field.","authors":"Zijiang Yang, Zhongwei Qiu, Chang Xu, Dongmei Fu","doi":"10.1109/TVCG.2024.3476331","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3476331","url":null,"abstract":"3D style transfer aims to generate stylized views of 3D scenes with specified styles, which requires high-quality generating and keeping multi-view consistency. Existing methods still suffer the challenges of high-quality stylization with texture details and stylization with multimodal guidance. In this paper, we reveal that the common training method of stylization with NeRF, which generates stylized multi-view supervision by 2D style transfer models, causes the same object in supervision to show various states (color tone, details, etc.) in different views, leading NeRF to tend to smooth the texture details, further resulting in low-quality rendering for 3D multi-style transfer. To tackle these problems, we propose a novel Multimodal-guided 3D Multi-style transfer of NeRF, termed MM-NeRF. First, MM-NeRF projects multimodal guidance into a unified space to keep the multimodal styles consistency and extracts multimodal features to guide the 3D stylization. Second, a novel multi-head learning scheme is proposed to relieve the difficulty of learning multi-style transfer, and a multi-view style consistent loss is proposed to track the inconsistency of multi-view supervision data. Finally, a novel incremental learning mechanism is proposed to generalize MM-NeRF to any new style with small costs. Extensive experiments on several real-world datasets show that MM-NeRF achieves high-quality 3D multi-style stylization with multimodal guidance, and keeps multi-view consistency and style consistency between multimodal guidance.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396311","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

Learn2Talk: 3D Talking Face Learns from 2D Talking Face. Learn2Talk：3D 会说话的人脸向 2D 会说话的人脸学习。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-07 DOI: 10.1109/TVCG.2024.3476275

Yixiang Zhuang, Baoping Cheng, Yao Cheng, Yuntao Jin, Renshuai Liu, Chengyang Li, Xuan Cheng, Jing Liao, Juncong Lin

{"title":"Learn2Talk: 3D Talking Face Learns from 2D Talking Face.","authors":"Yixiang Zhuang, Baoping Cheng, Yao Cheng, Yuntao Jin, Renshuai Liu, Chengyang Li, Xuan Cheng, Jing Liao, Juncong Lin","doi":"10.1109/TVCG.2024.3476275","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3476275","url":null,"abstract":"The speech-driven facial animation technology is generally categorized into two main types: 3D and 2D talking face. Both of these have garnered considerable research attention in recent years. However, to our knowledge, the research into 3D talking face has not progressed as deeply as that of 2D talking face, particularly in terms of lip-sync and perceptual mouth movements. The lip-sync necessitates an impeccable synchronization between mouth motion and speech audio. The speech perception derived from the perceptual mouth movements should resemble that of the driving audio. To mind the gap between the two sub-fields, we propose Learn2Talk, a learning framework that enhances 3D talking face network by integrating two key insights from the field of 2D talking face. Firstly, drawing inspiration from the audio-video sync network, we develop a 3D sync-lip expert model for the pursuit of lip-sync between audio and 3D facial motions. Secondly, we utilize a teacher model, carefully chosen from among 2D talking face methods, to guide the training of the audio-to-3D motions regression network, thereby increasing the accuracy of 3D vertex movements. Extensive experiments demonstrate the superiority of our proposed framework over state-of-the-art methods in terms of lip-sync, vertex accuracy and perceptual movements. Finally, we showcase two applications of our framework: audio-visual speech recognition and speech-driven 3D Gaussian Splatting-based avatar animation. The project page of this paper is: https://lkjkjoiuiu.github.io/Learn2Talk/.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396310","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

Parametric Body Reconstruction Based on a Single Front Scan Point Cloud. 基于单个前端扫描点云的参数化人体重构

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-07 DOI: 10.1109/TVCG.2024.3475414

Xihang Li, Guiqin Li, Ming Li, Haoju Song

{"title":"Parametric Body Reconstruction Based on a Single Front Scan Point Cloud.","authors":"Xihang Li, Guiqin Li, Ming Li, Haoju Song","doi":"10.1109/TVCG.2024.3475414","DOIUrl":"10.1109/TVCG.2024.3475414","url":null,"abstract":"Full-body 3D scanning simplifies the acquisition of digital body models. However, current systems are bulky, intricate, and costly, with strict clothing constraints. We propose a pipeline that combines inner body shape inference and parametric model registration for reconstructing the corresponding body model from a single front scan of a clothed body. Three networks modules (Scan2Front-Net, Front2Back-Net, and Inner2Corr-Net) with relatively independent functions are proposed for predicting front inner, back inner, and parametric model reference point clouds, respectively. We consider the back inner point cloud as an axial offset of the front inner point cloud and divide the body into 14 parts. This offset relationship is then learned within the same body parts to reduce the ambiguity of the inference. The predicted front and back inner point clouds are concatenated as inner body point cloud, and then reconstruction is achieved by registering the parametric body model through a point-to-point correspondence between the reference point cloud and the inner body point cloud. Qualitative and quantitative analysis show that the proposed method has significant advantages in terms of body shape completion and reconstruction body model accuracy.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142396312","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

RobustMap: Visual Exploration of DNN Adversarial Robustness in Generative Latent Space. RobustMap：生成潜空间中 DNN 对抗鲁棒性的可视化探索。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-03 DOI: 10.1109/TVCG.2024.3471551

Jie Li, Jielong Kuang

{"title":"RobustMap: Visual Exploration of DNN Adversarial Robustness in Generative Latent Space.","authors":"Jie Li, Jielong Kuang","doi":"10.1109/TVCG.2024.3471551","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3471551","url":null,"abstract":"The paper presents a novel approach to visualizing adversarial robustness (called robustness below) of deep neural networks (DNNs). Traditional tests only return a value reflecting a DNN's overall robustness across a fixed number of test samples. Unlike them, we use test samples to train a generative model (GM) and render a DNN's robustness distribution over infinite generated samples within the GM's latent space. The approach extends test samples, enabling users to obtain new test samples to improve feature coverage constantly. Moreover, the distribution provides more information about a DNN's robustness, enabling users to understand a DNN's robustness comprehensively. We propose three methods to resolve the challenges of realizing the approach. Specifically, we (1) map a GM's high-dimensional latent space onto a plane with less information loss for visualization, (2) design a network to predict a DNN's robustness on massive samples to speed up the distribution rendering, and (3) develop a system to supports users to explore the distribution from multiple perspectives. Subjective and objective experiment results prove the usability and effectiveness of the approach.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142373928","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

Smart Pipette: Elevating Laboratory Performance with Tactile Authenticity and Real-Time Feedback. 智能移液器：通过触感真实性和实时反馈提升实验室性能。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-02 DOI: 10.1109/TVCG.2024.3472837

Juan M Pieschacon, Maurizio Costabile, Andrew Cunningham, Joanne Zucco, Stewart Von Itzstein, Ross T Smith

{"title":"Smart Pipette: Elevating Laboratory Performance with Tactile Authenticity and Real-Time Feedback.","authors":"Juan M Pieschacon, Maurizio Costabile, Andrew Cunningham, Joanne Zucco, Stewart Von Itzstein, Ross T Smith","doi":"10.1109/TVCG.2024.3472837","DOIUrl":"10.1109/TVCG.2024.3472837","url":null,"abstract":"Mastering the correct use of laboratory equipment is a fundamental skill for undergraduate science students involved in laboratory-based training. However, hands-on laboratory time is often limited, and remote students may struggle as their absence from the physical lab limits their skill development. An air-displacement micropipette was selected for our initial investigation, as accuracy and correct technique are essential in generating reliable assay data. Handling small liquid volumes demands hand dexterity and practice to achieve proficiency. This research assesses the importance of tactile authenticity during training by faithfully replicating the micropipette's key physical and operational characteristics. We developed a custom haptic training approach called 'Smart Pipette' which promotes accurate operation and enhances laboratory dexterity training. A comparative user study with 34 participants evaluated the effectiveness of the Smart Pipette custom haptic device against training with off-the-shelf hardware, specifically the Quest VR hand controller, which was chosen because it is held mid-air similar to a laboratory micropipette. Both training conditions are integrated with the same self-paced virtual simulation displayed on a computer screen, offering clear video instructions and realtime guidance. Results demonstrated that participants trained with the Smart Pipette custom haptic exhibited increased accuracy and precision while making fewer errors than those trained with off-the-shelf hardware. The Smart Pipette and the Quest VR controller had no significant differences in cognitive load and system usability scores. Tactile authentic interaction devices address challenges faced by online learners, while their applicability extends to traditional classrooms, where real-time feedback significantly enhances overall training performance outcomes.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367962","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

Client-Designer Negotiation in Data Visualization Projects. 数据可视化项目中的客户与设计者谈判。

IEEE transactions on visualization and computer graphics Pub Date : 2024-10-02 DOI: 10.1109/TVCG.2024.3467189

Elsie Lee-Robbins, Arran Ridley, Eytan Adar

{"title":"Client-Designer Negotiation in Data Visualization Projects.","authors":"Elsie Lee-Robbins, Arran Ridley, Eytan Adar","doi":"10.1109/TVCG.2024.3467189","DOIUrl":"https://doi.org/10.1109/TVCG.2024.3467189","url":null,"abstract":"Data visualization designers and clients need to communicate effectively with each other to achieve a successful project. Unlike a personal or solo project, working with a client introduces a layer of complexity to the process. Client and designer might have different ideas about what is an acceptable solution that would satisfy the goals and constraints of the project. Thus, the client-designer relationship is an important part of the design process. To better understand the relationship, we conducted an interview study with 12 data visualization designers. We develop a model of a client-designer project space consisting of three aspects: surfacing project goals, agreeing on resource allocation, and creating a successful design. For each aspect, designer and client have their own mental model of how they envision the project. Disagreements between these models can be resolved by negotiation that brings them closer to alignment. We identified three main negotiation strategies to navigate the project space: 1) expanding the project space to consider more potential options, 2) constraining the project space to narrow in on the boundaries, and 3) shifting the project space to different options. We discuss client-designer collaboration as a negotiated relationship, with opportunities and challenges for each side. We suggest ways to mitigate challenges to avoid friction from developing into conflict.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142367961","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