ACM Transactions on Graphics最新文献_第2页

Reliable Iterative Dynamics: A Versatile Method for Fast and Robust Simulation 可靠迭代动力学：一种快速鲁棒仿真的通用方法

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-05-05 DOI: 10.1145/3734518

Jia-Ming Lu, Shi-Min Hu

{"title":"Reliable Iterative Dynamics: A Versatile Method for Fast and Robust Simulation","authors":"Jia-Ming Lu, Shi-Min Hu","doi":"10.1145/3734518","DOIUrl":"https://doi.org/10.1145/3734518","url":null,"abstract":"Simulating stiff materials has long posed formidable challenges for traditional physics-based solvers. Explicit time integration schemes demand prohibitively small time steps, while implicit methods necessitate an excessive number of iterations to converge, often yielding visually objectionable transient configurations in the early iterations, severely limiting their real-time applicability. Position-based dynamics techniques can efficiently simulate stiff constraints but are inherently restricted to constraint-based formulations, curtailing their versatility. We present ”Reliable Iterative Dynamics” (RID), a novel iterative solver that introduces a dual descent framework with theoretical guarantees for visual reliability at each iteration, while maintaining fast and stable convergence even for extremely stiff systems. Our core innovation is an iterative method that circumvents the need for numerous iterations or small time steps to handle stiff materials robustly. Experimental evaluations demonstrate our method’s ability to handle a wide range of materials, from soft to infinitely rigid, while producing visually reliable results even with large time steps and minimal iterations. The versatile formulation allows seamless integration with diverse simulation paradigms like the finite element method, material point method, smoothed particle hydrodynamics, and incremental potential contact for applications ranging from elastic body simulations to fluids and collision handling.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"197 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909836","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

End-to-end Surface Optimization for Light Control 光控制的端到端表面优化

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-05-02 DOI: 10.1145/3732284

Yuou Sun, Bailin Deng, Juyong Zhang

引用次数: 0

HoloChrome: Polychromatic Illumination for Speckle Reduction in Holographic Near-Eye Displays HoloChrome：用于全息近眼显示斑点减少的多色照明

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-28 DOI: 10.1145/3732935

Florian Andreas Schiffers, Grace Kuo, Nathan Matsuda, Douglas Lanman, Oliver Cossairt

{"title":"HoloChrome: Polychromatic Illumination for Speckle Reduction in Holographic Near-Eye Displays","authors":"Florian Andreas Schiffers, Grace Kuo, Nathan Matsuda, Douglas Lanman, Oliver Cossairt","doi":"10.1145/3732935","DOIUrl":"https://doi.org/10.1145/3732935","url":null,"abstract":"Holographic displays hold the promise of providing authentic depth cues, resulting in enhanced immersive visual experiences for near-eye applications. However, current holographic displays are hindered by speckle noise, which limits accurate reproduction of color and texture in displayed images. We present HoloChrome, a polychromatic holographic display framework designed to mitigate these limitations. HoloChrome utilizes an ultrafast, wavelength-adjustable laser and a dual-Spatial Light Modulator (SLM) architecture, enabling the multiplexing of a large set of discrete wavelengths across the visible spectrum. By leveraging spatial separation in our dual-SLM setup, we independently manipulate speckle patterns across multiple wavelengths. This novel approach effectively reduces speckle noise through incoherent averaging achieved by wavelength multiplexing, specifically by using a single SLM pattern to modulate multiple wavelengths simultaneously on one or more SLM devices. Our method is complementary to existing speckle reduction techniques, offering a new pathway to address this challenge. Furthermore, the use of polychromatic illumination broadens the achievable color gamut compared to traditional three-color primary holographic displays. Our simulations and tabletop experiments validate that HoloChrome significantly reduces speckle noise and expands the color gamut. These advancements enhance the performance of holographic near-eye displays, moving us closer to practical, immersive next-generation visual experiences.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"45 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884855","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

StructRe : Rewriting for Structured Shape Modeling StructRe：重写结构化形状建模

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-28 DOI: 10.1145/3732934

Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Taku Komura, Wenping Wang

{"title":"StructRe : Rewriting for Structured Shape Modeling","authors":"Jiepeng Wang, Hao Pan, Yang Liu, Xin Tong, Taku Komura, Wenping Wang","doi":"10.1145/3732934","DOIUrl":"https://doi.org/10.1145/3732934","url":null,"abstract":"Man-made 3D shapes are naturally organized in parts and hierarchies; such structures provide important constraints for shape reconstruction and generation. Modeling shape structures is difficult, because there can be multiple hierarchies for a given shape, causing ambiguity, and across different categories the shape structures are correlated with semantics, limiting generalization. We present StructRe , a structure rewriting system, as a novel approach to structured shape modeling. Given a 3D object represented by points and components, StructRe can rewrite it upward into more concise structures, or downward into more detailed structures; by iterating the rewriting process, hierarchies are obtained. Such a localized rewriting process enables probabilistic modeling of ambiguous structures and robust generalization across object categories. We train StructRe on PartNet data and show its generalization to cross-category and multiple object hierarchies, and test its extension to ShapeNet. We also demonstrate the benefits of probabilistic and generalizable structure modeling for shape reconstruction, generation and editing tasks.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"27 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143884860","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

Policy-Space Diffusion for Physics-Based Character Animation 基于物理的角色动画策略空间扩散

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-25 DOI: 10.1145/3732285

Michele Rocca, Sune Darkner, Kenny Erleben, Sheldon Andrews

{"title":"Policy-Space Diffusion for Physics-Based Character Animation","authors":"Michele Rocca, Sune Darkner, Kenny Erleben, Sheldon Andrews","doi":"10.1145/3732285","DOIUrl":"https://doi.org/10.1145/3732285","url":null,"abstract":"Adapting motion to new contexts in digital entertainment often demands fast agile prototyping. State-of-the-art techniques use reinforcement learning policies for simulating the underlined motion in a physics engine. Unfortunately, policies typically fail on unseen tasks and it is too time-consuming to fine-tune the policy for every new morphological, environmental, or motion change. We propose a novel point of view on using policy networks as a representation of motion for physics-based character animation. Our policies are compact, tailored to individual motion tasks, and preserve similarity with nearby tasks. This allows us to view the space of all motions as a manifold of policies where sampling substitutes training. We obtain memory-efficient encoding of motion that leverages the characteristics of control policies such as being generative, and robust to small environmental changes. With this perspective, we can sample novel motions by directly manipulating weights and biases through a Diffusion Model. Our newly generated policies can adapt to previously unseen characters, potentially saving time in rapid prototyping scenarios. Our contributions include the introduction of Common Neighbor Policy regularization to constrain policy similarity during motion imitation training making them suitable for generative modeling; a Diffusion Model adaptation for diverse morphology; and an open policy dataset. The results show that we can learn non-linear transformations in the policy space from labeled examples, and conditionally generate new ones. In a matter of seconds, we sample a batch of policies for different conditions that show comparable motion fidelity metrics as their respective trained ones.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"6 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143875758","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

A Neural Particle Level Set Method for Dynamic Interface Tracking 动态界面跟踪的神经粒子水平集方法

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-21 DOI: 10.1145/3730399

Duowen Chen, Junwei Zhou, Bo Zhu

引用次数: 0

MoFlow: Motion-Guided Flows for Recurrent Rendered Frame Prediction MoFlow：用于循环渲染帧预测的运动引导流

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-18 DOI: 10.1145/3730400

Zhizhen Wu, Zhilong Yuan, Chenyu Zuo, Yazhen Yuan, Yifan PENG, Guiyang Pu, Rui Wang, Yuchi Huo

{"title":"MoFlow: Motion-Guided Flows for Recurrent Rendered Frame Prediction","authors":"Zhizhen Wu, Zhilong Yuan, Chenyu Zuo, Yazhen Yuan, Yifan PENG, Guiyang Pu, Rui Wang, Yuchi Huo","doi":"10.1145/3730400","DOIUrl":"https://doi.org/10.1145/3730400","url":null,"abstract":"Rendering realistic images in real-time on high-frame-rate display devices poses considerable challenges, even with advanced graphics cards. This stimulates a demand for frame prediction technologies to boost frame rates. The key to these algorithms is to exploit spatiotemporal coherence by warping rendered pixels with motion representations. However, existing motion estimation methods can suffer from low precision, high overhead, and incomplete support for visual effects. In this article, we present a rendered frame prediction framework with a novel motion representation, dubbed motion-guided flow (MoFlow) , aiming to overcome the intrinsic limitations of optical flow and motion vectors and precisely capture the dynamics of intricate geometries, lighting, and translucent objects. Notably, we construct MoFlows using a recurrent feature streaming network, which specializes in learning latent motion features from multiple frames. The results of extensive experiments demonstrate that, compared to state-of-the-art methods, our method achieves superior visual quality and temporal stability with lower latency. The recurrent mechanism allows our method to predict single or multiple consecutive frames, increasing the frame rate by over 2 ×. The proposed approach represents a flexible pipeline to meet the demands of various graphics applications, devices, and scenarios.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"10 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143849781","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

Patch-Grid : An Efficient and Feature-Preserving Neural Implicit Surface Representation 斑块-网格：高效且保留特征的神经隐式表面表示法

IF 6.2 1区计算机科学

ACM Transactions on Graphics Pub Date : 2025-04-08 DOI: 10.1145/3727142

Guying Lin, Lei Yang, Congyi Zhang, Hao Pan, Yuhan Ping, Guodong Wei, Taku Komura, John Keyser, Wenping Wang

{"title":"Patch-Grid : An Efficient and Feature-Preserving Neural Implicit Surface Representation","authors":"Guying Lin, Lei Yang, Congyi Zhang, Hao Pan, Yuhan Ping, Guodong Wei, Taku Komura, John Keyser, Wenping Wang","doi":"10.1145/3727142","DOIUrl":"https://doi.org/10.1145/3727142","url":null,"abstract":"Neural implicit representations are increasingly used to depict 3D shapes owing to their inherent smoothness and compactness, contrasting with traditional discrete representations. Yet, the multilayer perceptron (MLP) based neural representation, because of its smooth nature, rounds sharp corners or edges, rendering it unsuitable for representing objects with sharp features like CAD models. Moreover, neural implicit representations need long training times to fit 3D shapes. While previous works address these issues separately, we present a unified neural implicit representation called Patch-Grid , which efficiently fits complex shapes, preserves sharp features delineating different patches, and can also represent surfaces with open boundaries and thin geometric features. Patch-Grid learns a signed distance field (SDF) to approximate an encompassing surface patch of the shape with a learnable patch feature volume. To form sharp edges and corners in a CAD model, Patch-Grid merges the learned SDFs via the constructive solid geometry (CSG) approach. Core to the merging process is a novel merge grid design that organizes different patch feature volumes in a common octree structure. This design choice ensures robust merging of multiple learned SDFs by confining the CSG operations to localized regions. Additionally, it drastically reduces the complexity of the CSG operations in each merging cell, allowing the proposed method to be trained in seconds to fit a complex shape at high fidelity. Experimental results demonstrate that the proposed Patch-Grid representation is capable of accurately reconstructing shapes with complex sharp features, open boundaries, and thin geometric elements, achieving state-of-the-art reconstruction quality with high computational efficiency within seconds.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"183 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-04-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143805704","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

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