IEEE transactions on visualization and computer graphics最新文献_第9页

PERCEIV: A Multimodal Physiological Dataset of Visual Encodings for Adaptive InfoVis Interfaces. 一个多模态生理数据集的视觉编码自适应InfoVis接口。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-06 DOI: 10.1109/TVCG.2026.3721212

Sebastian Idesis, Mireia Masias Bruns, Angela Lopez Cardona, Parvin Emami, Saravanakumar Duraisamy, Luis A Leiva, Ioannis Arapakis

{"title":"PERCEIV: A Multimodal Physiological Dataset of Visual Encodings for Adaptive InfoVis Interfaces.","authors":"Sebastian Idesis, Mireia Masias Bruns, Angela Lopez Cardona, Parvin Emami, Saravanakumar Duraisamy, Luis A Leiva, Ioannis Arapakis","doi":"10.1109/TVCG.2026.3721212","DOIUrl":"10.1109/TVCG.2026.3721212","url":null,"abstract":"We contribute PERCEIV (PERCeption of visual Encodings in InfoVis), a large-scale dataset to support studies of user responses to visual encoding variants as a foundation for adaptive information visualization interfaces. The dataset includes recordings from 120 participants who completed tasks of varying difficulty, while simultaneous multimodal data were collected from brain, eye, and electrodermal activity. All data are time-synchronized and accompanied by relevant annotations and participant metadata. We release raw sensor streams together with fully processed event-aligned derivatives and rich trial-level annotations. We also provide baseline analyses and scripts for ex tracting cognitive load features across modalities. In addition, we make the code for data pre-processing, feature extraction, qual ity control, and training/evaluating baseline Machine Learning classifiers available. Standardized data splits and comprehensive documentation further enable reproducible benchmarking and rapid reuse. We hope our dataset will facilitate novel studies of sensor modalities, development of computational models, and deeper understanding of cognitive state dynamics in controlled information-processing contexts.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148687232","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

Spatio-Temporal Kernel Density Estimation for Streaming Time-Varying Data. 时变数据流的时空核密度估计。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-08-06 DOI: 10.1109/TVCG.2026.3721513

Aritra Bhowmick

{"title":"Spatio-Temporal Kernel Density Estimation for Streaming Time-Varying Data.","authors":"Aritra Bhowmick","doi":"10.1109/TVCG.2026.3721513","DOIUrl":"10.1109/TVCG.2026.3721513","url":null,"abstract":"Kernel density estimation (KDE) is a fundamental tool across statistics, data science, visualization and machine learning. In particular, it is widely used in analyzing and visualizing multi-dimensional data sets, including, e.g., geo-spatial data. Spatio-temporal KDE is an important extension to the basic KDE problem that also takes into account the temporal relation of the data points, and provides a method to visualize evolving data patterns over time. With the explosive growth of Big Data in both size and complexity, it is typically not feasible to run naive exact algorithms due to the extremely high run time and memory-space requirements. Especially challenging is the streaming setting where the data points arrive in an online fashion, one time step at a time, and we can only access the data in one pass with a fixed, sub-linear memory. Such a challenging setting renders most of the existing methods to be inadmissible. In this paper, we take on this challenge by providing scalable approximate frameworks with low memory footprints under the streaming setting. We present randomized algorithms for efficient computation of spatio-temporal KDE with strong theoretical guarantees and empirical support. Specifically, we introduce a weighted sampling scheme and discuss a random-feature-based sketching method, and analyze their complexity and use cases. The proposed methods are sub-linear in memory and do not require the knowledge of the length of the data stream in advance. Also, the methods provide nice trade-offs between speed and accuracy. Moreover, the sketching method can easily support deletions of data points from the stream, which is not trivial for most sampling methods to perform robustly. In addition, we show how the sketching method can be easily used to answer aggregate density queries, with a computational advantage over any sampling approaches. Experiments on synthetic and real world datasets demonstrate the quality and efficiency of these proposed techniques.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148687148","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

Geometry-Semantics Co-Regularization for Gaussian Splatting in Indoor Reconstruction. 室内重建中高斯飞溅的几何-语义协同正则化。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-30 DOI: 10.1109/TVCG.2026.3718564

Haihong Xiao, Jianan Zou, Yanan Zhang, Wenxiong Kang, Ying He, Wei Jia

{"title":"Geometry-Semantics Co-Regularization for Gaussian Splatting in Indoor Reconstruction.","authors":"Haihong Xiao, Jianan Zou, Yanan Zhang, Wenxiong Kang, Ying He, Wei Jia","doi":"10.1109/TVCG.2026.3718564","DOIUrl":"10.1109/TVCG.2026.3718564","url":null,"abstract":"Recent advances in 3D Gaussian Splatting (3DGS) have significantly advanced indoor scene reconstruction, a key capability for AR/VR, robotics, and autonomous systems. However, existing methods that rely on 2D monocular priors often produce artifacts such as holes and unnatural protrusions due to insufficient or weak constraints. To address this limitation, we propose a geometry-semantics co-regularization framework that jointly optimizes geometry and semantics within 3DGS. On the geometric side, we introduce a multi-level geometric optimization strategy with two key contributions. First, inspired by neural implicit surface representations, we design a dual implicit architecture that integrates Implicit Moving Least Squares (IMLS) and Signed Distance Functions (SDFs). This structure enables mutual reinforcement between the two representations via local geometric approximation and global normal constraints, improving fine-grained surface modeling. Second, we develop a view-dependent normal consistency constraint that combines 2D observations with SDF gradients to refine surface accuracy. On the semantic side, we develop a multi-view semantic consistency supervision to regularize the semantic distributions of Gaussian primitives, ensuring cross-view consistency for Gaussians corresponding to the same semantic category or instance. Extensive experiments on the Replica and MuSHRoom datasets demonstrate that our approach achieves state-of-the-art geometric reconstruction quality and rendering fidelity.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148633374","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

DVD: Scaffolding Non-experts' Ideation of Data Videos through Co-creation with AI. DVD：搭建非专家通过与人工智能共同创作的数据视频构思。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-29 DOI: 10.1109/TVCG.2026.3717858

Xiaozhu Hu, Yuchao Zhuo, Bingyuan Wang, Zeyu Wang, Mingming Fan, Tristan Braud, Xian Xu

{"title":"DVD: Scaffolding Non-experts' Ideation of Data Videos through Co-creation with AI.","authors":"Xiaozhu Hu, Yuchao Zhuo, Bingyuan Wang, Zeyu Wang, Mingming Fan, Tristan Braud, Xian Xu","doi":"10.1109/TVCG.2026.3717858","DOIUrl":"10.1109/TVCG.2026.3717858","url":null,"abstract":"Data videos are an increasingly popular storytelling medium, effectively communicating data-driven insights to diverse audiences. However, designing compelling data videos requires not only domain knowledge of the data but also expertise in cinematic design, posing substantial challenges for non-experts in design. To study how AI can support non-experts in addressing such challenges during early-stage design, we introduce ideate-through-cocreate, a design concept in which users' ideation is scaffolded through co-creation with generative AI. We instantiate this concept by proposing Data Video Designer (DVD), a proof-of-concept system that supports the ideation of data video opening scenes. DVD facilitates non-experts' ideation processes by generating exemplary video scripts and visuals from high-level topic and data summaries, referencing relevant design guidelines, and integrating accurate visualizations derived from their own data into the generated scenes. Through this workflow, DVD turns abstract cinematic guidelines and AI-generated materials into actionable ideation support. A workshop and a comparative study demonstrated the usefulness of the structured co-creation support provided by DVD within the scope of opening-scene design and revealed the creative potential in balancing the data authenticity and stylized expression. These findings provide initial evidence for the potential of structured human-AI co-creation to support data video ideation.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148623322","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

Consistent 4D Appearance Editing with Gaussian Splatting. 一致的4D外观编辑高斯飞溅。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-28 DOI: 10.1109/TVCG.2026.3717152

Xiaosheng He, Fenglin Liu, Lin Gao, Yu-Kun Lai, Zhongang Cai, Lei Yang, Yi Xu, Guosheng Lin

{"title":"Consistent 4D Appearance Editing with Gaussian Splatting.","authors":"Xiaosheng He, Fenglin Liu, Lin Gao, Yu-Kun Lai, Zhongang Cai, Lei Yang, Yi Xu, Guosheng Lin","doi":"10.1109/TVCG.2026.3717152","DOIUrl":"10.1109/TVCG.2026.3717152","url":null,"abstract":"Editing dynamic scenes with 4D Gaussian Splatting (4DGS) is often hampered by spatiotemporal inconsistencies, or \"Gaussian drifting\", which degrades edit quality and temporal coherence. We identify that these artifacts stem from two distinct sources: foundational inaccuracies in the initial scene reconstruction, and the disruption of learned trajectories during the editing process itself. To address this, we propose a comprehensive framework that systematically tackles both sources of inconsistency. To solve reconstruction-induced errors, we introduce a novel prior-guided, multi-stage reconstruction pipeline that fuses geometric and motion priors to build a physically plausible and temporally stable foundation. To solve editing-induced errors, we further apply a universal trajectory-preserving technique, which safeguards high-quality motion by decoupling the appearance optimization from the learned deformation. Experiments demonstrate that by systematically addressing both the reconstruction and editing phases, our method achieves state-of-the-art, temporally consistent editing on a wide range of dynamic scenes where previous monolithic approaches fail.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148611321","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

Signal or Noise? How User Movement and Sensor Placement Impact Physiological Data Collection in 6DoF Virtual Reality. 信号还是噪音？用户运动和传感器放置如何影响6DoF虚拟现实的生理数据收集。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-20 DOI: 10.1109/TVCG.2026.3715380

Matthieu Blanchard, Bertrand Massot, Pierre Raimbaud, Guillaume Lavoue

{"title":"Signal or Noise? How User Movement and Sensor Placement Impact Physiological Data Collection in 6DoF Virtual Reality.","authors":"Matthieu Blanchard, Bertrand Massot, Pierre Raimbaud, Guillaume Lavoue","doi":"10.1109/TVCG.2026.3715380","DOIUrl":"10.1109/TVCG.2026.3715380","url":null,"abstract":"To evaluate user experience in Virtual Reality (VR), researchers are increasingly using physiological data, such as electrocardiograms (ECG) and electrodermal activity (EDA), in conjunction with questionnaires. However, in 6-Degrees-of-Freedom VR, user movements and interactions can introduce noise on captured signals, making them potentially unusable. This noise depends on the movements performed and on sensor positions. Unfortunately, no study has addressed this issue, significantly hindering the effective use of physiological data in VR. To address this gap, we conducted a user study to evaluate the effects of sensor positions and user movements on signal quality, as well as sensor comfort and intrusiveness. We compared three positions for EDA and two for ECG on 36 users, who performed a series of five gestures. Results indicate that positioning EDA sensors on the palm offers the best balance between signal quality and user comfort. For ECG, a position closer to the heart is better. Most tested movements affect quality, with the worst being when users close their hand (for ECG and EDA) and move their arm (for ECG), where sensors are located. Based on these findings, we provide recommendations for the effective use of EDA and ECG sensors in 6DoF VR.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148537571","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

FunHOI: Annotation-Free 3D Hand-Object Interaction Generation via Functional Text Guidance. FunHOI：通过功能文本引导生成无注释的3D手-对象交互。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-10 DOI: 10.1109/TVCG.2026.3712181

Yongqi Tian, Xueyu Sun, Haoyuan He, Jianlei Wang, Caigui Jiang

{"title":"FunHOI: Annotation-Free 3D Hand-Object Interaction Generation via Functional Text Guidance.","authors":"Yongqi Tian, Xueyu Sun, Haoyuan He, Jianlei Wang, Caigui Jiang","doi":"10.1109/TVCG.2026.3712181","DOIUrl":"10.1109/TVCG.2026.3712181","url":null,"abstract":"Hand-object interaction (HOI) is central to human manipulation, but synthesizing functional 3D HOI remains challenging due to dexterous hand motion, complex contact patterns, and strong geometric ambiguity. Existing methods often rely on 3D HOI annotations, predefined object templates, or supervised interaction priors. Such requirements limit their applicability to open-ended functional grasping scenarios, where the same object may require different contact regions and poses depending on the intended use. We present FunHOI, an annotation-free two-stage framework for functional-intent-conditioned 3D HOI synthesis from natural-language prompts. FunHOI represents each prompt using action-object-purpose semantics and first translates the functional intent into a semantically aligned 2D HOI guidance image through the Functional Grasp Generator (FGG). Rather than learning a direct text-to-3D generative prior, FunHOI reconstructs the hand and object from this 2D semantic guidance and composes them in 3D using the Functional Grasp Refiner (FGR), which performs scale-aware alignment, object pose approximation, and contact-driven refinement to improve spatial consistency and physical plausibility. Extensive experiments show that FunHOI achieves competitive reconstruction quality, improved hand-object contact plausibility, and better alignment with functional intent, without requiring 3D HOI annotations.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":6.5,"publicationDate":"2026-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148426648","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

ArtCrafter: Text-Image Aligning Artistic Attribute Transfer via Embedding Reframing 通过嵌入重构实现文本-图像对齐的艺术属性转移。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-01 Epub Date: 2025-12-23 DOI: 10.1109/TVCG.2025.3647628

Nisha Huang;Kaer Huang;Yifan Pu;Jiangshan Wang;Jie Guo;Yiqiang Yan;Xiu Li;Tong-Yee Lee

{"title":"ArtCrafter: Text-Image Aligning Artistic Attribute Transfer via Embedding Reframing","authors":"Nisha Huang;Kaer Huang;Yifan Pu;Jiangshan Wang;Jie Guo;Yiqiang Yan;Xiu Li;Tong-Yee Lee","doi":"10.1109/TVCG.2025.3647628","DOIUrl":"10.1109/TVCG.2025.3647628","url":null,"abstract":"Recent years have witnessed significant advancements in text-guided style transfer, primarily attributed to innovations in diffusion models. These models excel in conditional guidance, utilizing text or images to direct the sampling process. Traditional style transfer focuses on low-level visual features, such as brushstroke textures and color distributions, and appears more like applying an artistic filter to an image. Artistic attribute transfer, however, transcends the limitations of traditional style transfer by achieving the transfer of visual concepts from color and brushstrokes to high level aesthetic attributes such as composition, pose, and key semantic elements, resulting in more natural outcomes. Therefore, we propose an innovative text-to-image artistic attribute transfer framework named ArtCrafter. Specifically, we introduce an attention-based style extraction module, meticulously engineered to capture the subtle artistic attribute elements within an image. This module features a multi-layer architecture that leverages the capabilities of perceiver attention mechanisms to integrate fine-grained information. Additionally, we present a novel text-image aligning augmentation component that adeptly balances control over both modalities, enabling the model to efficiently map image and text embeddings into a shared feature space. We achieve this through attention operations that enable smooth information flow between modalities. Lastly, we incorporate an explicit modulation that seamlessly blends multimodal enhanced embeddings with original embeddings through an embedding reframing design, empowering the model to generate diverse outputs. Extensive experiments demonstrate that ArtCrafter yields impressive results in visual stylization, exhibiting exceptional levels of artistic attribute intensity, controllability, and diversity.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"32 7","pages":"4959-4972"},"PeriodicalIF":6.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145822431","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

How Scale Breaks “Normalized Stress” and KL Divergence: Rethinking Quality Metrics 尺度如何打破“标准化压力”和KL分歧：重新思考质量度量。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-01 Epub Date: 2026-01-26 DOI: 10.1109/TVCG.2026.3657654

Kiran Smelser;Kaviru Gunaratne;Jacob Miller;Stephen Kobourov

{"title":"How Scale Breaks “Normalized Stress” and KL Divergence: Rethinking Quality Metrics","authors":"Kiran Smelser;Kaviru Gunaratne;Jacob Miller;Stephen Kobourov","doi":"10.1109/TVCG.2026.3657654","DOIUrl":"10.1109/TVCG.2026.3657654","url":null,"abstract":"Complex, high-dimensional data is ubiquitous across many scientific disciplines, including machine learning, biology, and the social sciences. One of the primary methods of visualizing these datasets is with two-dimensional scatter plots that visually capture some properties of the data. Because visually determining the accuracy of these plots is challenging, researchers often use quality metrics to measure the projection's accuracy and faithfulness to the original data. One of the most commonly employed metrics, normalized stress, is sensitive to uniform scaling (stretching, shrinking) of the projection, despite this act not meaningfully changing anything about the projection. Another quality metric, the Kullback–Leibler (KL) divergence used in the popular t-Distributed Stochastic Neighbor Embedding (t-SNE) technique, is also susceptible to this scale sensitivity. We investigate the effect of scaling on stress and KL divergence analytically and empirically by showing just how much the values change and how this affects dimension reduction technique evaluations. We introduce a simple technique to make both metrics scale-invariant and show that it accurately captures expected behavior on a small benchmark.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"32 7","pages":"5020-5033"},"PeriodicalIF":6.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11364024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146055745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BAG: Body-Aligned 3D Wearable Asset Generation BAG：身体对齐3D可穿戴资产生成。

IF 6.5

IEEE transactions on visualization and computer graphics Pub Date : 2026-07-01 Epub Date: 2026-02-16 DOI: 10.1109/TVCG.2026.3665510

Zhongjin Luo;Yang Li;Mingrui Zhang;Senbo Wang;Han Yan;Xibin Song;Taizhang Shang;Wei Mao;Hongdong Li;Xiaoguang Han;Pan Ji

{"title":"BAG: Body-Aligned 3D Wearable Asset Generation","authors":"Zhongjin Luo;Yang Li;Mingrui Zhang;Senbo Wang;Han Yan;Xibin Song;Taizhang Shang;Wei Mao;Hongdong Li;Xiaoguang Han;Pan Ji","doi":"10.1109/TVCG.2026.3665510","DOIUrl":"10.1109/TVCG.2026.3665510","url":null,"abstract":"While recent advancements have demonstrated remarkable progress in general 3D shape generation, the challenge of automatically generating wearable 3D assets remains largely unexplored. To address this gap, we present BAG — a Body-aligned Asset Generation method that produces 3D wearable assets which can be automatically fitted onto given 3D human bodies. This is achieved by controlling the 3D generation process using human body shape and pose information. Specifically, we first construct a general single-image-to-consistent-multi-view diffusion model, and train it on the large-scale Objaverse dataset to ensure diversity and generalizability. We then train a body-conditioned multi-view ControlNet to guide the generator toward producing body-aligned multi-view images. The control signal leverages multi-view 2D projections of the target human body, where pixel values represent the XYZ coordinates of the body surface in a canonical space. The resulting body-conditioned multi-view diffusion outputs body-aligned images, which are subsequently fed into a native 3D diffusion model to reconstruct the 3D shape of the asset. Finally, we recover the similarity transformation using multi-view silhouette supervision and mitigate asset-body penetration using physics-based simulation, ensuring accurate asset fitting onto the target body. Experimental results demonstrate that our method significantly outperforms existing approaches in terms of prompt adherence, shape diversity, and shape quality.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"32 7","pages":"5235-5247"},"PeriodicalIF":6.5,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146208262","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