IEEE transactions on visualization and computer graphics最新文献_第10页

Delving into Invisible Semantics for Generalized One-shot Neural Human Rendering. 广义一次性神经人类渲染的不可见语义研究。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-22 DOI: 10.1109/TVCG.2025.3563229

Yihong Lin, Xuemiao Xu, Huaidong Zhang, Cheng Xu, Weijie Li, Yi Xie, Jing Qin, Shengfeng He

引用次数: 0

Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields. 基于神经体素场潜在三维扩散的部件感知形状生成。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-22 DOI: 10.1109/TVCG.2025.3562871

Yuhang Huang, Shilong Zou, Xinwang Liu, Kai Xu

{"title":"Part-aware Shape Generation with Latent 3D Diffusion of Neural Voxel Fields.","authors":"Yuhang Huang, Shilong Zou, Xinwang Liu, Kai Xu","doi":"10.1109/TVCG.2025.3562871","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3562871","url":null,"abstract":"This paper introduces a novel latent 3D diffusion model for generating neural voxel fields with precise partaware structures and high-quality textures. In comparison to existing methods, this approach incorporates two key designs to guarantee high-quality and accurate part-aware generation. On one hand, we introduce a latent 3D diffusion process for neural voxel fields, incorporating part-aware information into the diffusion process and allowing generation at significantly higher resolutions to capture rich textural and geometric details accurately. On the other hand, a part-aware shape decoder is introduced to integrate the part codes into the neural voxel fields, guiding accurate part decomposition and producing high-quality rendering results. Importantly, part-aware learning establishes structural relationships to generate texture information for similar regions, thereby facilitating high-quality rendering results. We evaluate our approach across eight different data classes through extensive experimentation and comparisons with state-of-the-art methods. The results demonstrate that our proposed method has superior generative capabilities in part-aware shape generation, outperforming existing state-of-the-art methods. Moreover, we have conducted image- and text-guided shape generation via the conditioned diffusion process, showcasing the advanced potential in multi-modal guided shape generation.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144065387","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

TrustME: A Context-Aware Explainability Model to Promote User Trust in Guidance. TrustME：一个情境感知的可解释性模型，以促进用户在指导中的信任。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-21 DOI: 10.1109/TVCG.2025.3562929

Maath Musleh, Renata G Raidou, Davide Ceneda

{"title":"TrustME: A Context-Aware Explainability Model to Promote User Trust in Guidance.","authors":"Maath Musleh, Renata G Raidou, Davide Ceneda","doi":"10.1109/TVCG.2025.3562929","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3562929","url":null,"abstract":"Guidance-enhanced approaches are used to support users in making sense of their data and overcoming challenging analytical scenarios. While recent literature underscores the value of guidance, a lack of clear explanations to motivate system interventions may still negatively impact guidance effectiveness. Hence, guidance-enhanced VA approaches require meticulous design, demanding contextual adjustments for developing appropriate explanations. Our paper discusses the concept of explainable guidance and how it impacts the user-system relationship-specifically, a user's trust in guidance within the VA process. We subsequently propose a model that supports the design of explainability strategies for guidance in VA. The model builds upon flourishing literature in explainable AI, available guidelines for developing effective guidance in VA systems, and accrued knowledge on user-system trust dynamics. Our model responds to challenges concerning guidance adoption and context-effectiveness by fostering trust through appropriately designed explanations. To demonstrate the model's value, we employ it in designing explanations within two existing VA scenarios. We also describe a design walk-through with a guidance expert to showcase how our model supports designers in clarifying the rationale behind system interventions and designing explainable guidance.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144061079","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

Virtualized Point Cloud Rendering. 虚拟化点云渲染。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-21 DOI: 10.1109/TVCG.2025.3562696

Jose A Collado, Alfonso Lopez, Juan M Jurado, J Roberto Jimenez

{"title":"Virtualized Point Cloud Rendering.","authors":"Jose A Collado, Alfonso Lopez, Juan M Jurado, J Roberto Jimenez","doi":"10.1109/TVCG.2025.3562696","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3562696","url":null,"abstract":"Remote sensing technologies, such as LiDAR, produce billions of points that commonly exceed the storage capacity of the GPU, restricting their processing and rendering. Level of detail (LoD) techniques have been widely investigated, but building the LoD structures is also time-consuming. This study proposes a GPU-driven culling system focused on determining the number of points visible in every frame. It can manipulate point clouds of any arbitrary size while maintaining a low memory footprint in both the CPU and GPU. Instead of organizing point clouds into hierarchical data structures, these are split into groups of points sorted using the Hilbert encoding. This alternative alleviates the occurrence of anomalous groups found in Morton curves. Instead of keeping the entire point cloud in the GPU, points are transferred on demand to ensure real-time capability. Accordingly, our solution can manipulate huge point clouds even in commodity hardware with low memory capacities. Moreover, hole filling is implemented to cover the gaps derived from insufficient density and our LoD system. Our proposal was evaluated with point clouds of up to 18 billion points, achieving an average of 80 frames per second (FPS) without perceptible quality loss. Relaxing memory constraints further enhances visual quality while maintaining an interactive frame rate. We assessed our method on real-world data, comparing it against three state-ofthe- art methods, demonstrating its ability to handle significantly larger point clouds. The code is available on https://github.com/Krixtalx/Nimbus.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144055946","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

GaussianHead: High-Fidelity Head Avatars With Learnable Gaussian Derivation 高斯头像：高保真头像与可学习的高斯推导。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-17 DOI: 10.1109/TVCG.2025.3561794

Jie Wang;Jiu-Cheng Xie;Xianyan Li;Feng Xu;Chi-Man Pun;Hao Gao

{"title":"GaussianHead: High-Fidelity Head Avatars With Learnable Gaussian Derivation","authors":"Jie Wang;Jiu-Cheng Xie;Xianyan Li;Feng Xu;Chi-Man Pun;Hao Gao","doi":"10.1109/TVCG.2025.3561794","DOIUrl":"10.1109/TVCG.2025.3561794","url":null,"abstract":"Creating lifelike 3D head avatars and generating compelling animations for diverse subjects remain challenging in computer vision. This paper presents GaussianHead, which models the active head based on anisotropic 3D Gaussians. Our method integrates a motion deformation field and a single-resolution tri-plane to capture the head's intricate dynamics and detailed texture. Notably, we introduce a customized derivation scheme for each 3D Gaussian, facilitating the generation of multiple “doppelgangers” through learnable parameters for precise position transformation. This approach enables efficient representation of diverse Gaussian attributes and ensures their precision. Additionally, we propose an inherited derivation strategy for newly added Gaussians to expedite training. Extensive experiments demonstrate GaussianHead's efficacy, achieving high-fidelity visual results with a remarkably compact model size (<inline-formula><tex-math>$approx 12$</tex-math></inline-formula> MB). Our method outperforms state-of-the-art alternatives in tasks such as reconstruction, cross-identity reenactment, and novel view synthesis.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"31 7","pages":"4141-4154"},"PeriodicalIF":0.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144056619","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

Progressive Multi-Plane Images Construction for Light Field Occlusion Removal. 渐进式多平面图像的光场遮挡去除。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-16 DOI: 10.1109/TVCG.2025.3561374

Shuo Zhang, Song Chang, Zhuoyu Shi, Youfang Lin

{"title":"Progressive Multi-Plane Images Construction for Light Field Occlusion Removal.","authors":"Shuo Zhang, Song Chang, Zhuoyu Shi, Youfang Lin","doi":"10.1109/TVCG.2025.3561374","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3561374","url":null,"abstract":"Recently, Light Field (LF) shows great potential in removing occlusion since the objects occluded in some views may be visible in other views. However, existing LF-based methods implicitly model each scene and can only remove objects that have positive disparities in one central views. In this paper, we propose a novel Progressive Multi-Plane Images (MPI) Construction method specifically designed for LF-based occlusion removal. Different from the previous MPI construction methods, we progressively construct MPIs layer by layer in order from near to far. In order to accurately model the current layer, the positions of foreground occlusions in the nearer layers are taken as occlusion prior. Specifically, we propose an Occlusion-Aware Attention Network to generate each layer of MPIs with reliable information in occluded regions. For each layer, occlusions in the current layer are filtered out so that the background is better recovered just using the visible views instead of the other occluded views. Then, by simply removing the layers containing occlusions and rendering MPIs in kinds of viewpoints, the occlusion removal results for different views are generated. Experiments on synthetic and real-world scenes show that our method outperforms state-of-the-art LF occlusion removal methods in quantitative and visual comparisons. Moreover, we also apply the proposed progressive MPI construction method to the view synthesis task. The occlusion edges in our synthesized views achieve significantly better quality, which also verifies that our method can better model the occluded regions.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144059990","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

Modeling Wireframe Meshes with Discrete Equivalence Classes. 用离散等价类建模线框网格。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-16 DOI: 10.1109/TVCG.2025.3561370

Pengyun Qiu, Rulin Chen, Peng Song, Ying He

{"title":"Modeling Wireframe Meshes with Discrete Equivalence Classes.","authors":"Pengyun Qiu, Rulin Chen, Peng Song, Ying He","doi":"10.1109/TVCG.2025.3561370","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3561370","url":null,"abstract":"We study a problem of modeling wireframe meshes where the vertices and edges fall into a set of discrete equivalence classes, respectively. This problem is motivated by the need of fabricating large wireframe structures at lower cost and faster speed since both nodes (thickened vertices) and rods (thickened edges) can be mass-produced. Given a 3D shape represented as a wireframe mesh, our goal is to compute a set of template vertices and a set of template edges, whose instances can be used to produce a fabricable wireframe mesh that approximates the input shape. To achieve this goal, we propose a computational approach that generates the template vertices and template edges by iteratively clustering and optimizing the mesh vertices and edges. At the clustering stage, we cluster mesh vertices and edges according to their shape and length, respectively. At the optimization stage, we first locally optimize the mesh to reduce the number of clusters of vertices and/or edges, and then globally optimize the mesh to reduce the intra-cluster variance for vertices and edges, while facilitating fabricability of the wireframe mesh. We demonstrate that our approach is able to model wireframe meshes with various shapes and topologies, compare it with three state-of-the-art approaches to show its superiority, and validate fabricability of our results by making three physical prototypes.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144063608","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

MonoRelief: Recovering 2.5D Relief from a Single Image. MonoRelief：从单个图像中恢复2.5D浮雕。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-16 DOI: 10.1109/TVCG.2025.3561361

Lipeng Gao, Yu-Wei Zhang, Mingqiang Wei, Hui Liu, Yanzhao Chen, Huadong Qiu, Caiming Zhang

引用次数: 0

Flexible and Probabilistic Topology Tracking with Partial Optimal Transport. 部分最优传输的柔性概率拓扑跟踪。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-15 DOI: 10.1109/TVCG.2025.3561300

Mingzhe Li, Xinyuan Yan, Lin Yan, Tom Needham, Bei Wang

{"title":"Flexible and Probabilistic Topology Tracking with Partial Optimal Transport.","authors":"Mingzhe Li, Xinyuan Yan, Lin Yan, Tom Needham, Bei Wang","doi":"10.1109/TVCG.2025.3561300","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3561300","url":null,"abstract":"In this paper, we present a flexible and probabilistic framework for tracking topological features in time-varying scalar fields using merge trees and partial optimal transport. Merge trees are topological descriptors that record the evolution of connected components in the sublevel sets of scalar fields. We present a new technique for modeling and comparing merge trees using tools from partial optimal transport. In particular, we model a merge tree as a measure network, that is, a network equipped with a probability distribution, and define a notion of distance on the space of merge trees inspired by partial optimal transport. Such a distance offers a new and flexible perspective for encoding intrinsic and extrinsic information in the comparative measures of merge trees. More importantly, it gives rise to a partial matching between topological features in time-varying data, thus enabling flexible topology tracking for scientific simulations. Furthermore, such partial matching may be interpreted as probabilistic coupling between features at adjacent time steps, which gives rise to probabilistic tracking graphs. We derive a stability result for our distance and provide numerous experiments indicating the efficacy of our framework in extracting meaningful feature tracks.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144015156","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

FLINT: Learning-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization. FLINT：基于学习的流量估计和科学集成可视化的时间插值。

IEEE transactions on visualization and computer graphics Pub Date : 2025-04-15 DOI: 10.1109/TVCG.2025.3561091

Hamid Gadirov, Jos B T M Roerdink, Steffen Frey

{"title":"FLINT: Learning-based Flow Estimation and Temporal Interpolation for Scientific Ensemble Visualization.","authors":"Hamid Gadirov, Jos B T M Roerdink, Steffen Frey","doi":"10.1109/TVCG.2025.3561091","DOIUrl":"https://doi.org/10.1109/TVCG.2025.3561091","url":null,"abstract":"We present FLINT (learning-based FLow estimation and temporal INTerpolation), a novel deep learning-based approach to estimate flow fields for 2D+time and 3D+time scientific ensemble data. FLINT can flexibly handle different types of scenarios with (1) a flow field being partially available for some members (e.g., omitted due to space constraints) or (2) no flow field being available at all (e.g., because it could not be acquired during an experiment). The design of our architecture allows to flexibly cater to both cases simply by adapting our modular loss functions, effectively treating the different scenarios as flow-supervised and flow-unsupervised problems, respectively (with respect to the presence or absence of ground-truth flow). To the best of our knowledge, FLINT is the first approach to perform flow estimation from scientific ensembles, generating a corresponding flow field for each discrete timestep, even in the absence of original flow information. Additionally, FLINT produces high-quality temporal interpolants between scalar fields. FLINT employs several neural blocks, each featuring several convolutional and deconvolutional layers. We demonstrate performance and accuracy for different usage scenarios with scientific ensembles from both simulations and experiments.","PeriodicalId":94035,"journal":{"name":"IEEE transactions on visualization and computer graphics","volume":"PP ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144029348","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