Graphical Models最新文献

LDM: Large tensorial SDF model for textured mesh generation LDM：用于纹理网格生成的大张量SDF模型

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-21 DOI: 10.1016/j.gmod.2025.101271

Rengan Xie , Kai Huang , Xiaoliang Luo , Yizheng Chen , Lvchun Wang , Qi Wang , Qi Ye , Wei Chen , Wenting Zheng , Yuchi Huo

{"title":"LDM: Large tensorial SDF model for textured mesh generation","authors":"Rengan Xie , Kai Huang , Xiaoliang Luo , Yizheng Chen , Lvchun Wang , Qi Wang , Qi Ye , Wei Chen , Wenting Zheng , Yuchi Huo","doi":"10.1016/j.gmod.2025.101271","DOIUrl":"10.1016/j.gmod.2025.101271","url":null,"abstract":"<div><div>Previous efforts have managed to generate production-ready 3D assets from text or images. However, these methods primarily employ NeRF or 3D Gaussian representations, which are not adept at producing smooth, high-quality geometries required by modern rendering pipelines. In this paper, we propose LDM, a <strong>L</strong>arge tensorial S<strong>D</strong>F <strong>M</strong>odel, which introduces a novel feed-forward framework capable of generating high-fidelity, illumination-decoupled textured mesh from a single image or text prompts. We firstly utilize a multi-view diffusion model to generate sparse multi-view inputs from single images or text prompts, and then a transformer-based model is trained to predict a tensorial SDF field from these sparse multi-view image inputs. Finally, we employ a gradient-based mesh optimization layer to refine this model, enabling it to produce an SDF field from which high-quality textured meshes can be extracted. Extensive experiments demonstrate that our method can generate diverse, high-quality 3D mesh assets with corresponding decomposed RGB textures within seconds. The project code is available at <span><span>https://github.com/rgxie/LDM</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101271"},"PeriodicalIF":2.5,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144330266","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

Optimization of cross-derivatives for ribbon-based multi-sided surfaces 带状多面曲面交叉导数的优化

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-19 DOI: 10.1016/j.gmod.2025.101275

Erkan Gunpinar , A. Alper Tasmektepligil , Márton Vaitkus , Péter Salvi

引用次数: 0

VolumeDiffusion: Feed-forward text-to-3D generation with efficient volumetric encoder VolumeDiffusion：前馈文本到3d生成与高效的体积编码器

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-18 DOI: 10.1016/j.gmod.2025.101274

Zhicong Tang , Shuyang Gu , Chunyu Wang , Ting Zhang , Jianmin Bao , Dong Chen , Baining Guo

{"title":"VolumeDiffusion: Feed-forward text-to-3D generation with efficient volumetric encoder","authors":"Zhicong Tang , Shuyang Gu , Chunyu Wang , Ting Zhang , Jianmin Bao , Dong Chen , Baining Guo","doi":"10.1016/j.gmod.2025.101274","DOIUrl":"10.1016/j.gmod.2025.101274","url":null,"abstract":"<div><div>This work presents VolumeDiffusion, a novel feed-forward text-to-3D generation framework that directly synthesizes 3D objects from textual descriptions. It bypasses the conventional score distillation loss based or text-to-image-to-3D approaches. To scale up the training data for the diffusion model, a novel 3D volumetric encoder is developed to efficiently acquire feature volumes from multi-view images. The 3D volumes are then trained on a diffusion model for text-to-3D generation using a 3D U-Net. This research further addresses the challenges of inaccurate object captions and high-dimensional feature volumes. The proposed model, trained on the public Objaverse dataset, demonstrates promising outcomes in producing diverse and recognizable samples from text prompts. Notably, it empowers finer control over object part characteristics through textual cues, fostering model creativity by seamlessly combining multiple concepts within a single object. This research significantly contributes to the progress of 3D generation by introducing an efficient, flexible, and scalable representation methodology.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101274"},"PeriodicalIF":2.5,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144314598","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

Goal-oriented 3D pattern adjustment with machine learning 目标导向的3D模式调整与机器学习

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-17 DOI: 10.1016/j.gmod.2025.101272

Megha Shastry , Ye Fan , Clarissa Martins , Dinesh K. Pai

{"title":"Goal-oriented 3D pattern adjustment with machine learning","authors":"Megha Shastry , Ye Fan , Clarissa Martins , Dinesh K. Pai","doi":"10.1016/j.gmod.2025.101272","DOIUrl":"10.1016/j.gmod.2025.101272","url":null,"abstract":"<div><div>Fit and sizing of clothing are fundamental problems in the field of garment design, manufacture, and retail. Here we propose new computational methods for adjusting the fit of clothing on realistic models of the human body by interactively modifying desired <em>fit attributes</em>. Clothing fit represents the relationship between the body and the garment, and can be quantified using physical fit attributes such as ease and pressure on the body. However, the relationship between pattern geometry and such fit attributes is notoriously complex and nonlinear, requiring deep pattern making expertise to adjust patterns to achieve fit goals. Such attributes can be computed by physically based simulations, using soft avatars. Here we propose a method to learn the relationship between the fit attributes and the space of 2D pattern edits. We demonstrate our method via interactive tools that directly edit fit attributes in 3D and instantaneously predict the corresponding pattern adjustments. The approach has been tested with a range of garment types, and validated by comparing with physical prototypes. Our method introduces an alternative way to directly express fit adjustment goals, making pattern adjustment more broadly accessible. As an additional benefit, the proposed approach allows pattern adjustments to be systematized, enabling better communication and audit of decisions.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"140 ","pages":"Article 101272"},"PeriodicalIF":2.5,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144298108","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

SEDFMNet: A Simple and Efficient Unsupervised Functional Map for Shape Correspondence Based on Deconstruction SEDFMNet：一种简单高效的基于解构的形状对应的无监督函数映射

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-01 DOI: 10.1016/j.gmod.2025.101270

Haojun Xu , Qinsong Li , Ling Hu , Shengjun Liu , Haibo Wang , Xinru Liu

{"title":"SEDFMNet: A Simple and Efficient Unsupervised Functional Map for Shape Correspondence Based on Deconstruction","authors":"Haojun Xu , Qinsong Li , Ling Hu , Shengjun Liu , Haibo Wang , Xinru Liu","doi":"10.1016/j.gmod.2025.101270","DOIUrl":"10.1016/j.gmod.2025.101270","url":null,"abstract":"<div><div>In recent years, deep functional maps (DFM) have emerged as a leading learning-based framework for non-rigid shape-matching problems, offering diverse network architectures for this domain. This richness also makes exploring better and novel design beliefs for existing powerful DFM components to promote performance meaningful and engaging. This paper delves into this problem and successfully produces the SEDFMNet, a simple yet highly efficient DFM pipeline. To achieve this, we systematically deconstruct the core modules of the general DFM framework and analyze key design choices in existing approaches to identify the most critical components through extensive experiments. By reassembling these crucial components, we culminate in developing our SEDFMNet, which features a simpler structure than conventional DFM pipelines while delivering superior performance. Our approach is rigorously validated through comprehensive experiments on diverse datasets, where the SEDFMNet consistently achieves state-of-the-art results, even in challenging scenarios such as non-isometric shape matching and shape matching with topological noise. Our work offers fresh insights into DFM research and opens new avenues for advancing this field.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101270"},"PeriodicalIF":2.5,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144203918","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

FastClothGNN: Optimizing message passing in Graph Neural Networks for accelerating real-time cloth simulation FastClothGNN：优化图神经网络中的消息传递，加速实时布料模拟

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-06-01 DOI: 10.1016/j.gmod.2025.101273

Yang Zhang, Kailuo Yu, Xinyu Zhang

引用次数: 0

DC-APIC: A decomposed compatible affine particle in cell transfer scheme for non-sticky solid–fluid interactions in MPM DC-APIC：在MPM中非粘性固-液相互作用的细胞转移方案中分解的兼容仿射粒子

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-05-25 DOI: 10.1016/j.gmod.2025.101269

Chenhui Wang , Jianyang Zhang , Chen Li , Changbo Wang

{"title":"DC-APIC: A decomposed compatible affine particle in cell transfer scheme for non-sticky solid–fluid interactions in MPM","authors":"Chenhui Wang , Jianyang Zhang , Chen Li , Changbo Wang","doi":"10.1016/j.gmod.2025.101269","DOIUrl":"10.1016/j.gmod.2025.101269","url":null,"abstract":"<div><div>Despite the material point method (MPM) provides a unified particle simulation framework for coupling of different materials, MPM suffers from sticky numerical artifacts, which is inherently restricted to sticky and no-slip interactions. In this paper, we propose a novel transfer scheme called Decomposed Compatible Affine Particle in Cell (DC-APIC) within the MPM framework for simulating the two-way coupled interaction between elastic solids and incompressible fluids under free-slip boundary conditions on a unified background grid. Firstly, we adopt particle-grid compatibility to describe the relationship between grid nodes and particles at the fluid–solid interface, which serves as the guideline for subsequent particle–grid–particle transfers. Then we develop a phase-field gradient method to track the compatibility and normal directions at the interface. Secondly, to facilitate automatic MPM collision resolution during solid–fluid coupling, in the proposed DC-APIC integrator, the tangential component will not be transferred between incompatible grid nodes to prevent velocity smoothing in another phase, while the normal component is transferred without limitations. Finally, our comprehensive results confirm that our approach effectively reduces diffusion and unphysical viscosity compared to traditional MPM.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101269"},"PeriodicalIF":2.5,"publicationDate":"2025-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134591","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

Human perception faithful curve reconstruction based on persistent homology and principal curve 基于持久同调和主曲线的人类感知忠实曲线重建

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-05-24 DOI: 10.1016/j.gmod.2025.101267

Yu Chen, Hongwei Lin, Yifan Xing

引用次数: 0

RMAvatar: Photorealistic human avatar reconstruction from monocular video based on rectified mesh-embedded Gaussians RMAvatar：基于校正网格嵌入高斯函数的单目视频真人头像重建

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-05-24 DOI: 10.1016/j.gmod.2025.101266

Sen Peng , Weixing Xie , Zilong Wang , Xiaohu Guo , Zhonggui Chen , Baorong Yang , Xiao Dong

{"title":"RMAvatar: Photorealistic human avatar reconstruction from monocular video based on rectified mesh-embedded Gaussians","authors":"Sen Peng , Weixing Xie , Zilong Wang , Xiaohu Guo , Zhonggui Chen , Baorong Yang , Xiao Dong","doi":"10.1016/j.gmod.2025.101266","DOIUrl":"10.1016/j.gmod.2025.101266","url":null,"abstract":"<div><div>We introduce RMAvatar, a novel human avatar representation with Gaussian splatting embedded on mesh to learn clothed avatar from a monocular video. We utilize the explicit mesh geometry to represent motion and shape of a virtual human and implicit appearance rendering with Gaussian Splatting. Our method consists of two main modules: Gaussian initialization module and Gaussian rectification module. We embed Gaussians into triangular faces and control their motion through the mesh, which ensures low-frequency motion and surface deformation of the avatar. Due to the limitations of LBS formula, the human skeleton is hard to control complex non-rigid transformations. We then design a pose-related Gaussian rectification module to learn fine-detailed non-rigid deformations, further improving the realism and expressiveness of the avatar. We conduct extensive experiments on public datasets, and RMAvatar shows state-of-the-art performance on both rendering quality and quantitative evaluations. Please see our project page at <span><span>https://rm-avatar.github.io</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":55083,"journal":{"name":"Graphical Models","volume":"139 ","pages":"Article 101266"},"PeriodicalIF":2.5,"publicationDate":"2025-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144123954","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

Semantics-aware human motion generation from audio instructions 从音频指令生成语义感知的人体运动

IF 2.5 4区计算机科学

Graphical Models Pub Date : 2025-05-23 DOI: 10.1016/j.gmod.2025.101268

Zi-An Wang , Shihao Zou , Shiyao Yu , Mingyuan Zhang , Chao Dong

引用次数: 0