ACM Transactions on Graphics最新文献_第2页

B4M : B reaking Low-Rank Adapter for M aking Content-Style Customization B4M：用于制作内容风格定制的低级别适配器

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-05 DOI: 10.1145/3728461

Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang, Oliver Deussen, Weiming Dong, Jintao Li, Tong-Yee Lee

{"title":"B4M : B reaking Low-Rank Adapter for M aking Content-Style Customization","authors":"Yu Xu, Fan Tang, Juan Cao, Yuxin Zhang, Oliver Deussen, Weiming Dong, Jintao Li, Tong-Yee Lee","doi":"10.1145/3728461","DOIUrl":"https://doi.org/10.1145/3728461","url":null,"abstract":"Personalized generation paradigms empower designers to customize visual intellectual properties with the help of textual descriptions by adapting pre-trained text-to-image models on a few images. Recent studies focus on simultaneously customizing content and detailed visual style in images but often struggle with entangling the two. In this study, we reconsider the customization of content and style concepts from the perspective of parameter space construction. Unlike existing methods that utilize a shared parameter space for content and style learning, we propose a novel framework that separates the parameter space to facilitate individual learning of content and style by introducing “partly learnable projection” ( PLP ) matrices to separate the original adapters into divided sub-parameter spaces. A “ break-for-make ” customization learning pipeline based on PLP is proposed: we first break the original adapters into “up projection” and “down projection” for content and style concept under orthogonal prior and then make the entity parameter space by reconstructing the content and style PLPs matrices by using Riemannian precondition to adaptively balance content and style learning. Experiments on various styles, including textures, materials, and artistic style, show that our method outperforms state-of-the-art single/multiple concept learning pipelines regarding content-style-prompt alignment. Code is available at: https://github.com/ICTMCG/Break-for-make.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"79 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143782862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Neurally Integrated Finite Elements for Differentiable Elasticity on Evolving Domains 演化域上可微弹性的神经积分有限元

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-02 DOI: 10.1145/3727874

Gilles Daviet, Tianchang Shen, Nicholas Sharp, David I.W. Levin

引用次数: 0

Diffusing Winding Gradients (DWG): A Parallel and Scalable Method for 3D Reconstruction from Unoriented Point Clouds 扩散缠绕梯度（DWG）：一种从无方向点云进行三维重建的并行和可扩展方法

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-01 DOI: 10.1145/3727873

Weizhou Liu, Jiaze Li, Xuhui Chen, Fei Hou, Shiqing Xin, Xingce Wang, Zhongke Wu, Chen Qian, Ying He

{"title":"Diffusing Winding Gradients (DWG): A Parallel and Scalable Method for 3D Reconstruction from Unoriented Point Clouds","authors":"Weizhou Liu, Jiaze Li, Xuhui Chen, Fei Hou, Shiqing Xin, Xingce Wang, Zhongke Wu, Chen Qian, Ying He","doi":"10.1145/3727873","DOIUrl":"https://doi.org/10.1145/3727873","url":null,"abstract":"This paper presents Diffusing Winding Gradients (DWG) for reconstructing watertight surfaces from unoriented point clouds. Our method exploits the alignment between the gradients of screened generalized winding number (GWN) field–a robust variant of the standard GWN field– and globally consistent normals to orient points. Starting with an unoriented point cloud, DWG initially assigns a random normal to each point. It computes the corresponding sGWN field and extract a level set whose iso-value is the average GWN values across all input points. The gradients of this level set are then utilized to update the point normals. This cycle of recomputing the sGWN field and updating point normals is repeated until the sGWN level sets stabilize and their gradients cease to change. Unlike conventional methods, DWG does not rely on solving linear systems or optimizing objective functions, which simplifies its implementation and enhances its suitability for efficient parallel execution. Experimental results demonstrate that DWG significantly outperforms existing methods in terms of runtime performance. For large-scale models with 10 to 20 million points, our CUDA implementation on an NVIDIA GTX 4090 GPU achieves speeds 30-120 times faster than iPSR, the leading sequential method, tested on a high-end PC with an Intel i9 CPU. Furthermore, by employing a screened variant of GWN, DWG demonstrates enhanced robustness against noise and outliers, and proves effective for models with thin structures and real-world inputs with overlapping and misaligned scans. For source code and additional results, visit our project webpage: https://dwgtech.github.io/.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"50 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143757802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Fast Determination and Computation of Self-intersections for NURBS Surfaces NURBS曲面自交的快速确定与计算

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-03-31 DOI: 10.1145/3727620

Kai Li, Xiaohong Jia, Falai Chen

{"title":"Fast Determination and Computation of Self-intersections for NURBS Surfaces","authors":"Kai Li, Xiaohong Jia, Falai Chen","doi":"10.1145/3727620","DOIUrl":"https://doi.org/10.1145/3727620","url":null,"abstract":"Self-intersections of NURBS surfaces are unavoidable during the CAD modeling process, especially in operations such as offset or sweeping. The existence of self-intersections might cause problems in the latter simulation and manufacturing process. Therefore, fast detection of self-intersections of NURBS is highly demanded in industrial applications. Self-intersections are essentially singular points on the surface. Although there is a long history of exploring singular points in mathematics community, the fast and robust determination and computation of self-intersections have been a challenging problem in practice. In this paper, we construct an algebraic signature whose non-negativity is proven to be sufficient for excluding the existence of self-intersections from a global perspective. An efficient algorithm for determining the existence of self-intersections is provided by recursively using this signature. Once the self-intersection is detected, if necessary, the self-intersection locus can also be computed via a further recursively cross-use of this signature and the surface-surface intersection function. Various experiments and comparisons with existing methods, as well as geometry kernels, including OCCT and ACIS, validate the robustness and efficiency of our algorithm. We also adapt our algorithm to self-intersection elimination, self-intersection trimming, and applications in mesh generation, boolean operation, and shelling.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"57 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

SpotLessSplats: Ignoring Distractors in 3D Gaussian Splatting SpotLessSplats：忽略3D高斯飞溅中的干扰物

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-03-29 DOI: 10.1145/3727143

Sara Sabour, Lily Goli, George Kopanas, Mark Matthews, Dmitry Lagun, Leonidas Guibas, Alec Jacobson, David Fleet, Andrea Tagliasacchi

引用次数: 0

NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity 利用三维光弹性的神经应力张量断层扫描

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-03-22 DOI: 10.1145/3723873

Akshat Dave, Tianyi Zhang, Aaron Young, Ramesh Raskar, Wolfgang Heidrich, Ashok Veeraraghavan

{"title":"NeST: Neural Stress Tensor Tomography by leveraging 3D Photoelasticity","authors":"Akshat Dave, Tianyi Zhang, Aaron Young, Ramesh Raskar, Wolfgang Heidrich, Ashok Veeraraghavan","doi":"10.1145/3723873","DOIUrl":"https://doi.org/10.1145/3723873","url":null,"abstract":"Photoelasticity enables full-field stress analysis in transparent objects through stress-induced birefringence. Existing techniques are limited to 2D slices and require destructively slicing the object. Recovering the internal 3D stress distribution of the entire object is challenging as it involves solving a tensor tomography problem and handling phase wrapping ambiguities. We introduce NeST, an analysis-by-synthesis approach for reconstructing 3D stress tensor fields as neural implicit representations from polarization measurements. Our key insight is to jointly handle phase unwrapping and tensor tomography using a differentiable forward model based on Jones calculus. Our non-linear model faithfully matches real captures, unlike prior linear approximations. We develop an experimental multi-axis polariscope setup to capture 3D photoelasticity and experimentally demonstrate that NeST reconstructs the internal stress distribution for objects with varying shape and force conditions. Additionally, we showcase novel applications in stress analysis, such as visualizing photoelastic fringes by virtually slicing the object and viewing photoelastic fringes from unseen viewpoints. NeST paves the way for scalable non-destructive 3D photoelastic analysis.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"6 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143675239","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Kinematic Motion Retargeting for Contact-Rich Anthropomorphic Manipulations 用于富接触拟人操纵的运动学运动重定位

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-03-15 DOI: 10.1145/3723872

Arjun Sriram Lakshmipathy, Jessica Hodgins, Nancy Pollard

{"title":"Kinematic Motion Retargeting for Contact-Rich Anthropomorphic Manipulations","authors":"Arjun Sriram Lakshmipathy, Jessica Hodgins, Nancy Pollard","doi":"10.1145/3723872","DOIUrl":"https://doi.org/10.1145/3723872","url":null,"abstract":"Hand motion capture data is now relatively easy to obtain, even for complicated grasps; however, this data is of limited use without the ability to retarget it onto the hands of a specific character or robot. The target hand may differ dramatically in geometry, number of degrees of freedom (DOFs), or number of fingers. We present a simple, but effective framework capable of kinematically retargeting human hand-object manipulations from a publicly available dataset to diverse target hands through the exploitation of contact areas. We do so by formulating the retargeting operation as a non-isometric shape matching problem and use a combination of both surface contact and marker data to progressively estimate, refine, and fit the final target hand trajectory using inverse kinematics (IK). Foundational to our framework is the introduction of a novel shape matching process, which we show enables predictable and robust transfer of contact data over full manipulations (pre-grasp, pickup, in-hand re-orientation, and release) while providing an intuitive means for artists to specify correspondences with relatively few inputs. We validate our framework through demonstrations across five different hands and six motions of different objects. We additionally demonstrate a bimanual task, perform stress tests, and compare our method against existing hand retargeting approaches. Finally, we demonstrate our method enabling novel capabilities such as object substitution and the ability to visualize the impact of hand design choices over full trajectories.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"59 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143631261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Encoded Marker Clusters for Auto-Labeling in Optical Motion Capture 光学运动捕捉中自动标记的编码标记簇

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-02-10 DOI: 10.1145/3716847

Hao Wang, Taogang Hou, Tianhui Liu, Jiaxin Li, Tianmiao Wang

引用次数: 0

Direct Rendering of Intrinsic Triangulations 直接渲染内在三角形

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-02-03 DOI: 10.1145/3716314

Waldemar Celes

{"title":"Direct Rendering of Intrinsic Triangulations","authors":"Waldemar Celes","doi":"10.1145/3716314","DOIUrl":"https://doi.org/10.1145/3716314","url":null,"abstract":"Existing intrinsic triangulation frameworks represent powerful tools for geometry processing; however, they all require the extraction of the common subdivision between extrinsic and intrinsic triangulations for visualization and optimized data transfer. We describe an efficient and effective algorithm for directly rendering intrinsic triangulations that avoids extracting common subdivisions. Our strategy is to use GPU shaders to render the intrinsic triangulation while rasterizing extrinsic triangles. We rely on a point-location algorithm supported by a compact data structure, which requires only two values per extrinsic triangle to represent the correspondence between extrinsic and intrinsic triangulations. This data structure is easier to maintain than previous proposals while supporting all the standard topological operations for improving the intrinsic mesh quality, such as edge flips, triangle refinements, and vertex displacements. Computational experiments show that the proposed data structure is numerically robust and can process nearly degenerate triangulations. We also propose a meshless strategy to accurately transfer data from intrinsic to extrinsic triangulations without relying on the extraction of common subdivisions.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"38 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143083145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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

Texture Size Reduction Through Symmetric Overlap and Texture Carving 通过对称重叠和纹理雕刻来减小纹理尺寸

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-01-25 DOI: 10.1145/3714408

Julian Knodt, Xifeng Gao

{"title":"Texture Size Reduction Through Symmetric Overlap and Texture Carving","authors":"Julian Knodt, Xifeng Gao","doi":"10.1145/3714408","DOIUrl":"https://doi.org/10.1145/3714408","url":null,"abstract":"Maintaining memory efficient 3D assets is critical for game development due to size constraints for applications, and runtime costs such as GPU data transfers. While most prior work on 3D modeling focuses on reducing triangle count, few works focus on reducing texture sizes. We propose an automatic approach to reduce the texture size for 3D models while maintaining the rendered appearance of the original input. The two core components of our approach are: (1) Overlapping identical UV charts and folding mirrored regions within charts through an optimal transport optimization, and (2) Carving redundant and void texels in a UV-aware and texture-aware way without inverting the UV mesh. The first component creates additional void space, while the second removes void space, and their combination can greatly increase texels utilized by the UV mesh at lower texture resolutions. Our method is robust and general, and can process a 3D model with arbitrary UV layout and multiple textures without modifying the 3D mesh. We evaluate our approach on 110 models from the Google Scanned Object dataset and 64 models from Sketchfab. Compared to other approaches, ours has on average 1-3 dB PSNR higher rendering similarity and reduces pixelation in visual comparisons.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"58 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-01-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143035154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"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