IEEE transactions on visualization and computer graphics最新文献_第2页

Visagreement: Visualizing and Exploring Explanations (Dis)Agreement. 签证协议：可视化和探索解释（不）协议。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-04 DOI: 10.1109/TVCG.2025.3558074

Priscylla Silva, Vitoria Guardieiro, Brian Barr, Claudio Silva, Luis Gustavo Nonato

{"title":"Visagreement: Visualizing and Exploring Explanations (Dis)Agreement.","authors":"Priscylla Silva, Vitoria Guardieiro, Brian Barr, Claudio Silva, Luis Gustavo Nonato","doi":"10.1109/TVCG.2025.3558074","DOIUrl":"10.1109/TVCG.2025.3558074","url":null,"abstract":"The emergence of distinct machine learning explanation methods has leveraged a number of new issues to be investigated. The disagreement problem is one such issue, as there may be scenarios where the output of different explanation methods disagree with each other. Although understanding how often, when, and where explanation methods agree or disagree is important to increase confidence in the explanations, few works have been dedicated to investigating such a problem. In this work, we proposed Visagreement, a visualization tool designed to assist practitioners in investigating the disagreement problem. Visagreement builds upon metrics to quantitatively compare and evaluate explanations, enabling visual resources to uncover where and why methods mostly agree or disagree. The tool is tailored for tabular data with binary classification and focuses on local feature importance methods. In the provided use cases, Visagreement turned out to be effective in revealing, among other phenomena, how disagreements relate to the quality of the explanations and machine learning model accuracy, thus assisting users in deciding where and when to trust explanations. To assess the effectiveness and practical utility of Visagreement, we conducted an evaluation involving four experts. These experts assessed the tool's Effectiveness, Usability, and Impact on Decision-Making. The experts confirm the Visagreement tool's effectiveness and user-friendliness, making it a valuable asset for analyzing and exploring (dis)agreements.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143784664","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

StruGauAvatar: Learning Structured 3D Gaussians for Animatable Avatars from Monocular Videos. StruGauAvatar：从单目视频中学习可动画头像的结构化3D高斯。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-03 DOI: 10.1109/TVCG.2025.3557457

Yihao Zhi, Wanhu Sun, Jiahao Chang, Chongjie Ye, Wensen Feng, Xiaoguang Han

{"title":"StruGauAvatar: Learning Structured 3D Gaussians for Animatable Avatars from Monocular Videos.","authors":"Yihao Zhi, Wanhu Sun, Jiahao Chang, Chongjie Ye, Wensen Feng, Xiaoguang Han","doi":"10.1109/TVCG.2025.3557457","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3557457","url":null,"abstract":"In recent years, significant progress has been witnessed in the field of neural 3D avatar reconstruction. Among all related tasks, building an animatable avatar from monocular videos is one of the most challenging ones, yet it also has a wide range of applications. The \"animatable\" means that we need to transfer any arbitrary and unseen poses onto the avatar and generate new 3D videos. Thanks to the rise of the powerful representation of NeRF, generating a high-fidelity animatable avatar from videos has become easier and more accessible. Despite their impressive visual results, the substantial training and rendering overhead dramatically hamper their applications. 3D Gaussian Splatting, as a timely new representation, has demonstrated its high-quality and high-efficiency rendering. This has led to many concurrent works to introduce 3D-GS to animatable avatar building. Although they demonstrate very high-fidelity renderings for poses similar to the training video frames, poor results are produced when the poses are far from training. We argue that this is primarily because the Gaussian points lack structures. Thus, we suggest involving DMTet to represent the coarse geometry of the avatar. In our representation, the majority of Gaussian points are bound to the mesh vertices, while some free Gaussian is allowed to expand to better fit the given video. Furthermore, we develop a dual-space optimization framework to jointly optimize the DMTet, Gaussian points, and skinning weights under two spaces. In this sense, Gaussian points are deformed in a constrained way, which dramatically improves the generalization ability for unseen poses. This is well demonstrated via extensive experiments.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782363","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

Palette-based color harmonization. 基于调色板的色彩协调

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-02 DOI: 10.1109/TVCG.2025.3546210

Jianchao Tan, Jose Echevarria, Yotam Gingold

引用次数: 0

Parallelize Over Data Particle Advection: Participation, Ping Pong Particles, and Overhead. 数据粒子平流上的并行化：参与、乒乓粒子和开销。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-02 DOI: 10.1109/TVCG.2025.3557453

Zhe Wang, Kenneth Moreland, Matthew Larsen, James Kress, Hank Childs, David Pugmire

{"title":"Parallelize Over Data Particle Advection: Participation, Ping Pong Particles, and Overhead.","authors":"Zhe Wang, Kenneth Moreland, Matthew Larsen, James Kress, Hank Childs, David Pugmire","doi":"10.1109/TVCG.2025.3557453","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3557453","url":null,"abstract":"Particle advection is one of the foundational algorithms for visualization and analysis and is central to understanding vector fields common to scientific simulations. Achieving efficient performance with large data in a distributed memory setting is notoriously difficult. Because of its simplicity and minimized movement of large vector field data, the Parallelize over Data (POD) algorithm has become a de facto standard. Despite its simplicity and ubiquitous usage, the scaling issues with the POD algorithm are known and have been described throughout the literature. In this paper, we describe a set of in-depth analyses of the POD algorithm that shed new light on the underlying causes for the poor performance of this algorithm. We designed a series of representative workloads to study the performance of the POD algorithm and executed them on a supercomputer while collecting timing and statistical data for analysis. we then performed two different types of analysis. In the first analysis, we introduce two novel metrics for measuring algorithmic efficiency over the course of a workload run. The second analysis was from the perspective of the particles being advected. Using particlecentric analysis, we identify that the overheads associated with particle movement between processes (not the communication itself) have a dramatic impact on the overall execution time. These overheads become particularly costly when flow features span multiple blocks, resulting in repeated particle circulation (which we term \"ping pong particles\") between blocks. Our findings shed important light on the underlying causes of poor performance and offer directions for future research to address these limitations.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143775238","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

GaussEdit: Adaptive 3D Scene Editing with Text and Image Prompts. GaussEdit：自适应3D场景编辑与文本和图像提示。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-01 DOI: 10.1109/TVCG.2025.3556745

Zhenyu Shu, Junlong Yu, Kai Chao, Shiqing Xin, Ligang Liu

{"title":"GaussEdit: Adaptive 3D Scene Editing with Text and Image Prompts.","authors":"Zhenyu Shu, Junlong Yu, Kai Chao, Shiqing Xin, Ligang Liu","doi":"10.1109/TVCG.2025.3556745","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3556745","url":null,"abstract":"This paper presents GaussEdit, a framework for adaptive 3D scene editing guided by text and image prompts. GaussEdit leverages 3D Gaussian Splatting as its backbone for scene representation, enabling convenient Region of Interest selection and efficient editing through a three-stage process. The first stage involves initializing the 3D Gaussians to ensure high-quality edits. The second stage employs an Adaptive Global-Local Optimization strategy to balance global scene coherence and detailed local edits and a category-guided regularization technique to alleviate the Janus problem. The final stage enhances the texture of the edited objects using a sophisticated image-to-image synthesis technique, ensuring that the results are visually realistic and align closely with the given prompts. Our experimental results demonstrate that GaussEdit surpasses existing methods in editing accuracy, visual fidelity, and processing speed. By successfully embedding user-specified concepts into 3D scenes, GaussEdit is a powerful tool for detailed and user-driven 3D scene editing, offering significant improvements over traditional methods.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766269","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

Deep Frequency Awareness Functional Maps for Robust Shape Matching. 鲁棒形状匹配的深度频率感知函数映射。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-01 DOI: 10.1109/TVCG.2025.3556209

Feifan Luo, Qinsong Li, Ling Hu, Haibo Wang, Haojun Xu, Xinru Liu, Shengjun Liu, Hongyang Chen

{"title":"Deep Frequency Awareness Functional Maps for Robust Shape Matching.","authors":"Feifan Luo, Qinsong Li, Ling Hu, Haibo Wang, Haojun Xu, Xinru Liu, Shengjun Liu, Hongyang Chen","doi":"10.1109/TVCG.2025.3556209","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3556209","url":null,"abstract":"Traditional deep functional map frameworks are widely used for 3D shape matching; however, many methods fail to adaptively capture the relevant frequency information required for functional map estimation in complex scenarios, leading to poor performance, especially under significant deformations. To address these challenges, we propose a novel unsupervised learning-based framework, Deep Frequency Awareness Functional Maps (DFAFM), specifically designed to tackle diverse shape-matching problems. Our approach introduces the Spectral Filter Operator Preservation constraint, which ensures the preservation of critical frequency information. These constraints promote frequency awareness by learning a set of spectral filters and incorporating them as a loss function to jointly supervise the functional maps, pointwise maps, and spectral filters. The spectral filters are constructed using orthonormal Jacobi polynomials with learnable coefficients, enabling adaptive and efficient frequency representation. Furthermore, we propose a refinement strategy that leverages the learned spectral filters and constraints to enhance the accuracy of the final pointwise map. Extensive experiments conducted on multiple benchmark datasets demonstrate that our method outperforms state-of-the-art approaches, particularly in challenging scenarios involving non-isometric deformations and inconsistent topology. Our code is available at https://github.com/LuoFeifan77/DeepFAFM.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766266","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

IllumiDiff: Indoor Illumination Estimation from a Single Image with Diffusion Model. IllumiDiff：室内照度估算从单幅图像与扩散模型。

IEEE transactions on visualization and computer graphics Pub Date : 2025-03-31 DOI: 10.1109/TVCG.2025.3553853

Shiyuan Shen, Zhongyun Bao, Wenju Xu, Chunxia Xiao

{"title":"IllumiDiff: Indoor Illumination Estimation from a Single Image with Diffusion Model.","authors":"Shiyuan Shen, Zhongyun Bao, Wenju Xu, Chunxia Xiao","doi":"10.1109/TVCG.2025.3553853","DOIUrl":"10.1109/TVCG.2025.3553853","url":null,"abstract":"Illumination estimation from a single indoor image is a promising yet challenging task. Existing indoor illumination estimation methods mainly regress lighting parameters or infer a panorama from a limited field-of-view image. Nevertheless, these methods fail to recover a panorama with both well-distributed illumination and detailed environment textures, leading to a lack of realism in rendering the embedded 3D objects with complex materials. This paper presents a novel multi-stage illumination estimation framework named IllumiDiff. Specifically, in Stage I, we first estimate illumination conditions from the input image, including the illumination distribution as well as the environmental texture of the scene. In Stage II, guided by the estimated illumination conditions, we design a conditional panoramic texture diffusion model to generate a high-quality LDR panorama. In Stage III, we leverage the illumination conditions to further reconstruct the LDR panorama to an HDR panorama. Extensive experiments demonstrate that our IllumiDiff can generate an HDR panorama with realistic illumination distribution and rich texture details from a single limited field-of-view indoor image. The generated panorama can produce impressive rendering results for the embedded 3D objects with various materials.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143766247","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

ViTon-GUN: Person-to-Person Virtual Try-on via Garment Unwrapping. ViTon-GUN：通过打开服装包装进行人对人虚拟试穿。

IEEE transactions on visualization and computer graphics Pub Date : 2025-03-28 DOI: 10.1109/TVCG.2025.3550776

Nannan Zhang, Zhenyu Xie, Zhengwentai Sun, Hairui Zhu, Zirong Jin, Nan Xiang, Xiaoguang Han, Song Wu

{"title":"ViTon-GUN: Person-to-Person Virtual Try-on via Garment Unwrapping.","authors":"Nannan Zhang, Zhenyu Xie, Zhengwentai Sun, Hairui Zhu, Zirong Jin, Nan Xiang, Xiaoguang Han, Song Wu","doi":"10.1109/TVCG.2025.3550776","DOIUrl":"10.1109/TVCG.2025.3550776","url":null,"abstract":"The image-based Person-to-Person (P2P) virtual try-on, involving the direct transfer of garments from one person to another, is one of the most promising applications of human-centric image generation. However, existing approaches struggle to accurately learn the clothing deformation when directly warping the garment from the source pose onto the target pose. To address this, we propose Person-to-Person virtual try-on via Garment UNwrapping, a novel framework dubbed as ViTon-GUN. Specifically, we divide the P2P task into two subtasks: Person-to-Garment (P2G) and Garment-to-Person (G2P). The P2G aims to unwrap the target garment from a source pose to a canonical representation based on A-Pose. In the P2G stage, we enable the implementation of a flow-based P2G scheme by introducing an A-Pose estimator and establishing comprehensive training conditions. Building upon this step-wise strategy, we introduce a novel pipeline for P2P try-on. Once trained, the P2G strategy can serve as a \"plug-and-play\" module, which efficiently adapts existing diffusion-based pre-trained G2P models to P2P try-on without further training. Quantitative and qualitative experiments demonstrate that our ViTon-GUN performs remarkably well on P2P try-on, even for dresses with intricate design details.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143735690","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

As-Rigid-As-Possible Deformation of Gaussian Radiance Fields. 高斯辐射场的 "尽可能刚性变形"（As-Rigid-As-Possible Deformation of Gaussian Radiance Fields）。

IEEE transactions on visualization and computer graphics Pub Date : 2025-03-27 DOI: 10.1109/TVCG.2025.3555404

Xinhao Tong, Tianjia Shao, Yanlin Weng, Yin Yang, Kun Zhou

{"title":"As-Rigid-As-Possible Deformation of Gaussian Radiance Fields.","authors":"Xinhao Tong, Tianjia Shao, Yanlin Weng, Yin Yang, Kun Zhou","doi":"10.1109/TVCG.2025.3555404","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3555404","url":null,"abstract":"3D Gaussian Splatting (3DGS) models radiance fields as sparsely distributed 3D Gaussians, providing a compelling solution to novel view synthesis at high resolutions and real-time frame rates. However, deforming objects represented by 3D Gaussians remains a challenging task. Existing methods deform a 3DGS object by editing Gaussians geometrically. These approaches ignore the fact that it is the radiance field that rasterizes and renders the final image. The inconsistency between the deformed 3D Gaussians and the desired radiance field inevitably leads to artifacts in the final results. In this paper, we propose an interactive method for as-rigid-as-possible (ARAP) deformation of the Gaussian radiance fields. Specifically, after performing geometric edits on the Gaussians, we further optimize Gaussians to ensure its rasterization yields a similar result as the deformed radiance field. To facilitate this objective, we design radial features to mathematically describe the radial difference before and after the deformation, which are densely sampled across the radiance field. Additionally, we propose an adaptive anisotropic spatial low-pass filter to prevent aliasing issues during sampling and to preserve the field with the varying non-uniform sampling intervals. Users can interactively employ this tool to achieve large-scale ARAP deformations of the radiance field. Since our method maintains the consistency of the Gaussian radiance field before and after deformation, it avoids artifacts that are common in existing 3DGS deformation frameworks. Meanwhile, our method keeps the high quality and efficiency of 3DGS in rendering.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733738","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

Multimodal Neural Acoustic Fields for Immersive Mixed Reality 沉浸式混合现实的多模态神经声场。

IEEE transactions on visualization and computer graphics Pub Date : 2025-03-26 DOI: 10.1109/TVCG.2025.3549898

Guaneen Tong;Johnathan Chi-Ho Leung;Xi Peng;Haosheng Shi;Liujie Zheng;Shengze Wang;Arryn Carlos O'Brien;Ashley Paula-Ann Neall;Grace Fei;Martim Gaspar;Praneeth Chakravarthula

{"title":"Multimodal Neural Acoustic Fields for Immersive Mixed Reality","authors":"Guaneen Tong;Johnathan Chi-Ho Leung;Xi Peng;Haosheng Shi;Liujie Zheng;Shengze Wang;Arryn Carlos O'Brien;Ashley Paula-Ann Neall;Grace Fei;Martim Gaspar;Praneeth Chakravarthula","doi":"10.1109/TVCG.2025.3549898","DOIUrl":"10.1109/TVCG.2025.3549898","url":null,"abstract":"We introduce multimodal neural acoustic fields for synthesizing spatial sound and enabling the creation of immersive auditory experiences from novel viewpoints and in completely unseen new environments, both virtual and real. Extending the concept of neural radiance fields to acoustics, we develop a neural network-based model that maps an environment's geometric and visual features to its audio characteristics. Specifically, we introduce a novel hybrid transformer-convolutional neural network to accomplish two core tasks: capturing the reverberation characteristics of a scene from audio-visual data, and generating spatial sound in an unseen new environment from signals recorded at sparse positions and orientations within the original scene. By learning to represent spatial acoustics in a given environment, our approach enables creation of realistic immersive auditory experiences, thereby enhancing the sense of presence in augmented and virtual reality applications. We validate the proposed approach on both synthetic and real-world visual-acoustic data and demonstrate that our method produces nonlinear acoustic effects such as reverberations, and improves spatial audio quality compared to existing methods. Furthermore, we also conduct subjective user studies and demonstrate that the proposed framework significantly improves audio perception in immersive mixed reality applications.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"31 5","pages":"3397-3407"},"PeriodicalIF":0.0,"publicationDate":"2025-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143733761","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