Computer Graphics Forum最新文献

SkinCells: Sparse Skinning using Voronoi Cells SkinCells：稀疏皮肤使用Voronoi细胞

4区计算机科学

Computer Graphics Forum Pub Date : 2026-04-14 DOI: 10.1111/cgf.70381

Egor Larionov, Igor Santesteban, Hsiao-yu Chen, Gene Wei-Chin Lin, Philipp Herholz, Ryan Goldade, Ladislav Kavan, Doug Roble, Tuur Stuyck

{"title":"SkinCells: Sparse Skinning using Voronoi Cells","authors":"Egor Larionov, Igor Santesteban, Hsiao-yu Chen, Gene Wei-Chin Lin, Philipp Herholz, Ryan Goldade, Ladislav Kavan, Doug Roble, Tuur Stuyck","doi":"10.1111/cgf.70381","DOIUrl":"https://doi.org/10.1111/cgf.70381","url":null,"abstract":"Abstract For decades, real‐time skinning has been the cornerstone of character animation in visual effects and games. Despite its importance, the creation of animatable digital assets remains a labor‐intensive manual process. Existing automated tools frequently struggle with intricate geometries, often necessitating significant manual refinement to reach production standards. We present a robust, fully automated method for generating high‐quality skinning weights from a standard mesh and skeleton in a canonical A‐ or T‐pose. Unlike traditional approaches, our framework offers direct sparsity controls to limit bone influences per vertex – a critical requirement for maintaining performance in large‐scale mobile environments. Furthermore, we address the challenge of Level‐of‐Detail (LoD) management by optimizing weights within a continuous spatial volume rather than on discrete vertices. This allows a single optimization pass to be applied seamlessly across multiple asset resolutions and variations. Central to our approach is a novel parameterized family of functions, we call SkinCells. We demonstrate that our method consistently produces stable, high‐quality results even in complex scenarios where standard biharmonic weight computations fail.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://arxiv.org/pdf/2506.14714","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147886596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Progressively Projected Newton's Method 渐进投影牛顿法

4区计算机科学

Computer Graphics Forum Pub Date : 2026-04-14 DOI: 10.1111/cgf.70386

José Antonio Fernández-Fernández, Fabian Löschner, Jan Bender

{"title":"Progressively Projected Newton's Method","authors":"José Antonio Fernández-Fernández, Fabian Löschner, Jan Bender","doi":"10.1111/cgf.70386","DOIUrl":"https://doi.org/10.1111/cgf.70386","url":null,"abstract":"Abstract Newton's Method is widely used to find the solution of complex non‐linear simulation problems. To guarantee a descent direction, it is common practice to clamp the negative eigenvalues of each element Hessian prior to assembly—a strategy known as Projected Newton (PN)—but this perturbation often hinders convergence. In this work, we observe that projecting only a small subset of element Hessians is sufficient to secure a descent direction. Building on this insight, we introduce Progressively Projected Newton (PPN), a novel variant of Newton's Method that uses the current iterate's residual to cheaply determine the subset of element Hessians to project. The benefit is twofold: most eigendecompositions are avoided and the global Hessian remains closer to its original form, reducing the number of Newton iterations. We compare PPN with PN and Project‐on‐Demand Newton (PDN) in a comprehensive set of experiments covering contact‐free and contact‐rich deformables, co‐dimensional and rigid‐body simulations, and a range of time step sizes, tolerances and resolutions. PPN reduces the amount of element projections in dynamic simulations by one order of magnitude while simultaneously improving convergence, consistently being the fastest solver in our benchmark.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147911397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles. 神经外科网络模式分析与第二代集成。

IF 2.6 4区计算机科学

Computer Graphics Forum Pub Date : 2026-04-01 DOI: 10.1111/cgf.70430

N Nipu, S Zhao, B Maharathi, J A Loeb, G E Marai

{"title":"Neurosurgery Network Pattern Analysis with 2nd Generation Ensembles.","authors":"N Nipu, S Zhao, B Maharathi, J A Loeb, G E Marai","doi":"10.1111/cgf.70430","DOIUrl":"10.1111/cgf.70430","url":null,"abstract":"Evolving measuring and computing capabilities, along with increasingly complex problems or models, are resulting in a new type of dataset: second-generation ensemble data. Like first-generation ensemble data, these data consist of large-scale, repeated measurements of the same process or phenomenon, and often have a spatial component. Unlike older datasets, they typically require the extraction and aggregation of novel complex features, which may be generated through direct measurements rather than simulations, and appear in a wider range of application domains, including neuroscience. We describe an interactive visual analysis solution for this type of second-generation ensemble data, related to the study and planning of surgical interventions in epilepsy treatment. As part of this solution, we introduce a dynamic community abstraction in conjunction with analysis algorithms for feature extraction and aggregation, registration techniques to correlate and project sample data, and custom visual encodings to support the analysis of conserved network patterns. A quantitative and qualitative evaluation with domain experts at four sites demonstrates the effectiveness of this solution. We discuss this approach and solution in the context of second-generation ensemble data analysis, along with the challenges of working with this type of data.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":" ","pages":""},"PeriodicalIF":2.6,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13410835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148618733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

L-VISP: LSTM Visualization for Interpretable Symptom Prediction in Patient Cohorts. L-VISP: LSTM可视化用于患者队列中可解释的症状预测。

IF 2.6 4区计算机科学

Computer Graphics Forum Pub Date : 2026-03-19 DOI: 10.1111/cgf.70314

C Floricel, Y Wang, A Wentzel, C D Fuller, G E Marai, M E Papka, G Canahuate

引用次数: 0

Arches: A Cycle‐Level Hardware Simulation Framework for Exploring Massively Parallel Ray Tracing Architectures arch：用于探索大规模并行光线追踪架构的周期级硬件仿真框架

4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-21 DOI: 10.1111/cgf.70212

Jacob Haydel, Gaurav Bhokare, Kaiping Zeng, Pengpei Hong, Sushant Kondguli, Brian Budge, Erik Brunvand, Cem Yuksel

{"title":"Arches: A Cycle‐Level Hardware Simulation Framework for Exploring Massively Parallel Ray Tracing Architectures","authors":"Jacob Haydel, Gaurav Bhokare, Kaiping Zeng, Pengpei Hong, Sushant Kondguli, Brian Budge, Erik Brunvand, Cem Yuksel","doi":"10.1111/cgf.70212","DOIUrl":"https://doi.org/10.1111/cgf.70212","url":null,"abstract":"Abstract We introduce Arches, a hardware simulation framework designed to explore and evaluate massively parallel ray‐tracing architectures. Operating at the cycle level, Arches captures detailed performance metrics, including computational throughput, on‐chip data movement across processors, caches, and off‐chip communication via an accurate memory system model. The framework is modular, allowing flexible configuration and interconnection of processor cores, caches, and custom hardware units, enabling easy exploration of diverse hardware architectures. Arches supports high‐performance parallel execution, simulating complex ray tracing workloads to image completion. It leverages the GNU toolchain, allowing users to write C++ software targeting both the simulated architecture and native execution for debugging, including support for custom instructions to control specialized hardware components. The framework provides comprehensive performance instrumentation, offering insights into time‐varying statistics across all modules and identifying performance bottlenecks. Our evaluations demonstrate that Arches delivers performance estimates closely matching real hardware, offering faster and more accurate simulations than existing open‐source hardware simulators. Its modularity also makes it a valuable tool for exploring alternative parallel computing strategies for high‐performance ray tracing, and its extensibility enables adaptation for other workloads or general‐purpose computation.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147921191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hierarchical Differentiable Fluid Simulation 分层可微流体模拟

4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-17 DOI: 10.1111/cgf.70226

Xiangyu Kong, Arnaud Schoentgen, Damien Rioux‐Lavoie, Paul G. Kry, Derek Nowrouzezahrai

引用次数: 0

TopoGen: Topology-Aware 3D Generation with Persistence Points TopoGen：具有持久点的拓扑感知3D生成

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70257

Jiangbei Hu, Ben Fei, Baixin Xu, Fei Hou, Shengfa Wang, Na Lei, Weidong Yang, Chen Qian, Ying He

引用次数: 0

LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation LayoutRectifier：基于优化的图形设计布局生成后处理

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70273

I-Chao Shen, Ariel Shamir, Takeo Igarashi

{"title":"LayoutRectifier: An Optimization-based Post-processing for Graphic Design Layout Generation","authors":"I-Chao Shen, Ariel Shamir, Takeo Igarashi","doi":"10.1111/cgf.70273","DOIUrl":"https://doi.org/10.1111/cgf.70273","url":null,"abstract":"Recent deep learning methods can generate diverse graphic design layouts efficiently. However, these methods often create layouts with flaws, such as misalignment, unwanted overlaps, and unsatisfied containment. To tackle this issue, we propose an optimization-based method called LayoutRectifier, which gracefully rectifies auto-generated graphic design layouts to reduce these flaws while minimizing deviation from the generated layout. The core of our method is a two-stage optimization. First, we utilize grid systems, which professional designers commonly use to organize elements, to mitigate misalignments through discrete search. Second, we introduce a novel box containment function designed to adjust the positions and sizes of the layout elements, preventing unwanted overlapping and promoting desired containment. We evaluate our method on content-agnostic and content-aware layout generation tasks and achieve better-quality layouts that are more suitable for downstream graphic design tasks. Our method complements learning-based layout generation methods and does not require additional training.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/cgf.70273","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PARC: A Two-Stage Multi-Modal Framework for Point Cloud Completion 点云补全的两阶段多模态框架

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70266

Yujiao Cai, Yuhao Su

{"title":"PARC: A Two-Stage Multi-Modal Framework for Point Cloud Completion","authors":"Yujiao Cai, Yuhao Su","doi":"10.1111/cgf.70266","DOIUrl":"https://doi.org/10.1111/cgf.70266","url":null,"abstract":"Point cloud completion is vital for accurate 3D reconstruction, yet real world scans frequently exhibit large structural gaps that compromise recovery. Meanwhile, in 2D vision, VAR (Visual Auto-Regression) has demonstrated that a coarse-to-fine “next-scale prediction” can significantly improve generation quality, inference speed, and generalization. Because this coarse-to-fine approach closely aligns with the progressive nature of filling missing geometry in point clouds, we were inspired to develop PARC (Patch-Aware Coarse-to-Fine Refinement Completion), a two-stage multimodal framework specifically designed for handling missing structures. In the pretraining stage, PARC leverages complete point clouds alongside a Patch-Aware Coarse-to-Fine Refinement (PAR) strategy and a Mixture-of-Experts (MoE) architecture to generate high-quality local fragments, thereby improving geometric structure understanding and feature representation quality. During finetuning, the model is adapted to partial scans, further enhancing its resilience to incomplete inputs. To address remaining uncertainties in areas with missing structure, we introduce a dual-branch architecture that incorporates image cues: point cloud and image features are extracted independently and then fused via the MoE with an alignment loss, allowing complementary modalities to guide reconstruction in occluded or missing regions. Experiments conducted on the ShapeNet-ViPC dataset show that PARC has achieved highly competitive performance. Code is available at https://github.com/caiyujiaocyj/PARC.","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297134","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PaMO: Parallel Mesh Optimization for Intersection-Free Low-Poly Modeling on the GPU PaMO: GPU上无交集低多边形建模的并行网格优化

IF 2.9 4区计算机科学

Computer Graphics Forum Pub Date : 2025-10-11 DOI: 10.1111/cgf.70267

Seonghun Oh, Xiaodi Yuan, Xinyue Wei, Ruoxi Shi, Fanbo Xiang, Minghua Liu, Hao Su

{"title":"PaMO: Parallel Mesh Optimization for Intersection-Free Low-Poly Modeling on the GPU","authors":"Seonghun Oh, Xiaodi Yuan, Xinyue Wei, Ruoxi Shi, Fanbo Xiang, Minghua Liu, Hao Su","doi":"10.1111/cgf.70267","DOIUrl":"https://doi.org/10.1111/cgf.70267","url":null,"abstract":"Reducing the triangle count in complex 3D models is a basic geometry preprocessing step in graphics pipelines such as efficient rendering and interactive editing. However, most existing mesh simplification methods exhibit a few issues. Firstly, they often lead to self-intersections during decimation, a major issue for applications such as 3D printing and soft-body simulation. Second, to perform simplification on a mesh in the wild, one would first need to perform re-meshing, which often suffers from surface shifts and losses of sharp features. Finally, existing re-meshing and simplification methods can take minutes when processing large-scale meshes, limiting their applications in practice. To address the challenges, we introduce a novel GPU-based mesh optimization approach containing three key components: (1) a parallel re-meshing algorithm to turn meshes in the wild into watertight, manifold, and intersection-free ones, and reduce the prevalence of poorly shaped triangles; (2) a robust parallel simplification algorithm with intersection-free guarantees; (3) an optimization-based safe projection algorithm to realign the simplified mesh with the input, eliminating the surface shift introduced by re-meshing and recovering the original sharp features. The algorithm demonstrates remarkable efficiency, simplifying a 2-million-face mesh to 20k triangles in 3 seconds on RTX4090. We evaluated the approach on the Thingi10K dataset and showcased its exceptional performance in geometry preservation and speed. https://seonghunn.github.io/pamo/","PeriodicalId":10687,"journal":{"name":"Computer Graphics Forum","volume":"44 7","pages":""},"PeriodicalIF":2.9,"publicationDate":"2025-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145297135","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0