Abdalla G. M. Ahmed, Matt Pharr, Victor Ostromoukhov, Hui Huang
Low-discrepancy sequences have seen widespread adoption in computer graphics thanks to the superior rates of convergence that they provide. Because rendering integrals often are comprised of products of lower-dimensional integrals, recent work has focused on developing sequences that are also well-distributed in lower-dimensional projections. To this end, we introduce a novel construction of binary-based (0, 4)-sequences; that is, progressive fully multi-stratified sequences of 4D points, and extend the idea to higher power-of-two dimensions. We further show that not only it is possible to nest lower-dimensional sequences in higher-dimensional ones—for example, embedding a (0, 2)-sequence within our (0, 4)-sequence—but that we can ensemble two (0, 2)-sequences into a (0, 4)-sequence, four (0, 4)-sequences into a (0,16)-sequence, and so on. Such sequences can provide excellent rates of convergence when integrals include lower-dimensional integration problems in 2, 4, 16,... dimensions. Our construction is based on using 2×2 block matrices as symbols to construct larger matrices that potentially generate a sequence with the target (0, s )-sequence in base s property. We describe how to search for suitable alphabets and identify two distinct, cross-related alphabets of block symbols, which we call s and z , hence SZ for the resulting family of sequences. Given the alphabets, we construct candidate generator matrices and search for valid sets of matrices. We then infer a simple recurrence formula to construct full-resolution (64-bit) matrices. Because our generator matrices are binary, they allow highly-efficient implementation using bitwise operations and can be used as a drop-in replacement for Sobol matrices in existing applications. We compare SZ sequences to state-of-the-art low discrepancy sequences, and demonstrate mean relative squared error improvements up to 1.93× in common rendering applications.
{"title":"SZ Sequences: Binary-Based (0, 2 q )-Sequences","authors":"Abdalla G. M. Ahmed, Matt Pharr, Victor Ostromoukhov, Hui Huang","doi":"10.1145/3763272","DOIUrl":"https://doi.org/10.1145/3763272","url":null,"abstract":"Low-discrepancy sequences have seen widespread adoption in computer graphics thanks to the superior rates of convergence that they provide. Because rendering integrals often are comprised of products of lower-dimensional integrals, recent work has focused on developing sequences that are also well-distributed in lower-dimensional projections. To this end, we introduce a novel construction of binary-based (0, 4)-sequences; that is, progressive fully multi-stratified sequences of 4D points, and extend the idea to higher power-of-two dimensions. We further show that not only it is possible to nest lower-dimensional sequences in higher-dimensional ones—for example, embedding a (0, 2)-sequence within our (0, 4)-sequence—but that we can ensemble two (0, 2)-sequences into a (0, 4)-sequence, four (0, 4)-sequences into a (0,16)-sequence, and so on. Such sequences can provide excellent rates of convergence when integrals include lower-dimensional integration problems in 2, 4, 16,... dimensions. Our construction is based on using 2×2 block matrices as symbols to construct larger matrices that potentially generate a sequence with the target (0, <jats:italic toggle=\"yes\">s</jats:italic> )-sequence in base <jats:italic toggle=\"yes\">s</jats:italic> property. We describe how to search for suitable alphabets and identify two distinct, cross-related alphabets of block symbols, which we call <jats:italic toggle=\"yes\">s</jats:italic> and <jats:italic toggle=\"yes\">z</jats:italic> , hence <jats:italic toggle=\"yes\">SZ</jats:italic> for the resulting family of sequences. Given the alphabets, we construct candidate generator matrices and search for valid sets of matrices. We then infer a simple recurrence formula to construct full-resolution (64-bit) matrices. Because our generator matrices are binary, they allow highly-efficient implementation using bitwise operations and can be used as a drop-in replacement for Sobol matrices in existing applications. We compare SZ sequences to state-of-the-art low discrepancy sequences, and demonstrate mean relative squared error improvements up to 1.93× in common rendering applications.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"33 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673862","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}
Gradient-domain rendering estimates image-space gradients using correlated sampling, which can be combined with color information to reconstruct smoother and less noisy images. While simple ℒ 2 reconstruction is unbiased, it often leads to visible artifacts. In contrast, most recent reconstruction methods based on learned or handcrafted techniques improve visual quality but introduce bias, leaving the development of practically unbiased reconstruction approaches relatively underexplored. In this work, we propose a generalized framework for unbiased reconstruction in gradient-domain rendering. We first derive the unbiasedness condition under a general formulation that linearly combines pixel colors and gradients. Based on this unbiasedness condition, we design a practical algorithm 1 that minimizes image variance while strictly satisfying unbiasedness. Experimental results demonstrate that our method not only guarantees unbiasedness but also achieves superior quality compared to existing unbiased and slightly biased reconstruction methods.
{"title":"Generalized Unbiased Reconstruction for Gradient-Domain Rendering","authors":"Difei Yan, Zengyu Li, Lifan Wu, Kun Xu","doi":"10.1145/3763297","DOIUrl":"https://doi.org/10.1145/3763297","url":null,"abstract":"Gradient-domain rendering estimates image-space gradients using correlated sampling, which can be combined with color information to reconstruct smoother and less noisy images. While simple ℒ <jats:sub>2</jats:sub> reconstruction is unbiased, it often leads to visible artifacts. In contrast, most recent reconstruction methods based on learned or handcrafted techniques improve visual quality but introduce bias, leaving the development of practically unbiased reconstruction approaches relatively underexplored. In this work, we propose a generalized framework for unbiased reconstruction in gradient-domain rendering. We first derive the unbiasedness condition under a general formulation that linearly combines pixel colors and gradients. Based on this unbiasedness condition, we design a practical algorithm <jats:sup>1</jats:sup> that minimizes image variance while strictly satisfying unbiasedness. Experimental results demonstrate that our method not only guarantees unbiasedness but also achieves superior quality compared to existing unbiased and slightly biased reconstruction methods.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"203 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673868","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}
Pengfei Wang, Qiujie Dong, Fangtian Liang, Hao Pan, Lei Yang, Congyi Zhang, Guying Lin, Caiming Zhang, Yuanfeng Zhou, Changhe Tu, Shiqing Xin, Alla Sheffer, Xin Li, Wenping Wang
Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeling the shape of a neural implicit surface, especially as the zero-level set of a neural signed distance function (SDF), with sparse geometric control is still a challenging task. Sparse input shape control typically includes 3D curve networks or, more generally, 3D curve sketches, which are unstructured and cannot be connected to form a curve network, and therefore more difficult to deal with. While 3D curve networks or curve sketches provide intuitive shape control, their sparsity and varied topology pose challenges in generating high-quality surfaces to meet such curve constraints. In this paper, we propose NeuVAS, a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control, including unstructured 3D curve sketches as well as connected 3D curve networks. Specifically, we introduce a smoothness term based on a functional of surface curvatures to minimize shape variation of the zero-level set surface of a neural SDF. We also develop a new technique to faithfully model G0 sharp feature curves as specified in the input curve sketches. Comprehensive comparisons with the state-of-the-art methods demonstrate the significant advantages of our method.
{"title":"NeuVAS: Neural Implicit Surfaces for Variational Shape Modeling","authors":"Pengfei Wang, Qiujie Dong, Fangtian Liang, Hao Pan, Lei Yang, Congyi Zhang, Guying Lin, Caiming Zhang, Yuanfeng Zhou, Changhe Tu, Shiqing Xin, Alla Sheffer, Xin Li, Wenping Wang","doi":"10.1145/3763331","DOIUrl":"https://doi.org/10.1145/3763331","url":null,"abstract":"Neural implicit shape representation has drawn significant attention in recent years due to its smoothness, differentiability, and topological flexibility. However, directly modeling the shape of a neural implicit surface, especially as the zero-level set of a neural signed distance function (SDF), with sparse geometric control is still a challenging task. Sparse input shape control typically includes 3D curve networks or, more generally, 3D curve sketches, which are unstructured and cannot be connected to form a curve network, and therefore more difficult to deal with. While 3D curve networks or curve sketches provide intuitive shape control, their sparsity and varied topology pose challenges in generating high-quality surfaces to meet such curve constraints. In this paper, we propose NeuVAS, a variational approach to shape modeling using neural implicit surfaces constrained under sparse input shape control, including unstructured 3D curve sketches as well as connected 3D curve networks. Specifically, we introduce a smoothness term based on a functional of surface curvatures to minimize shape variation of the zero-level set surface of a neural SDF. We also develop a new technique to faithfully model <jats:italic toggle=\"yes\">G</jats:italic> <jats:sup>0</jats:sup> sharp feature curves as specified in the input curve sketches. Comprehensive comparisons with the state-of-the-art methods demonstrate the significant advantages of our method.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"1 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673920","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}
Lihan Jiang, Yucheng Mao, Linning Xu, Tao Lu, Kerui Ren, Yichen Jin, Xudong Xu, Mulin Yu, Jiangmiao Pang, Feng Zhao, Dahua Lin, Bo Dai
We introduce AnySplat, a feed-forward network for novel-view synthesis from uncalibrated image collections. In contrast to traditional neural-rendering pipelines that demand known camera poses and per-scene optimization, or recent feed-forward methods that buckle under the computational weight of dense views—our model predicts everything in one shot. A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance, and the corresponding camera intrinsics and extrinsics for each input image. This unified design scales effortlessly to casually captured, multi-view datasets without any pose annotations. In extensive zero-shot evaluations, AnySplat matches the quality of pose-aware baselines in both sparse- and dense-view scenarios while surpassing existing pose-free approaches. Moreover, it greatly reduces rendering latency compared to optimization-based neural fields, bringing real-time novel-view synthesis within reach for unconstrained capture settings. Project page: https://city-super.github.io/anysplat/.
{"title":"AnySplat: Feed-forward 3D Gaussian Splatting from Unconstrained Views","authors":"Lihan Jiang, Yucheng Mao, Linning Xu, Tao Lu, Kerui Ren, Yichen Jin, Xudong Xu, Mulin Yu, Jiangmiao Pang, Feng Zhao, Dahua Lin, Bo Dai","doi":"10.1145/3763326","DOIUrl":"https://doi.org/10.1145/3763326","url":null,"abstract":"We introduce AnySplat, a feed-forward network for novel-view synthesis from uncalibrated image collections. In contrast to traditional neural-rendering pipelines that demand known camera poses and per-scene optimization, or recent feed-forward methods that buckle under the computational weight of dense views—our model predicts everything in one shot. A single forward pass yields a set of 3D Gaussian primitives encoding both scene geometry and appearance, and the corresponding camera intrinsics and extrinsics for each input image. This unified design scales effortlessly to casually captured, multi-view datasets without any pose annotations. In extensive zero-shot evaluations, AnySplat matches the quality of pose-aware baselines in both sparse- and dense-view scenarios while surpassing existing pose-free approaches. Moreover, it greatly reduces rendering latency compared to optimization-based neural fields, bringing real-time novel-view synthesis within reach for unconstrained capture settings. Project page: https://city-super.github.io/anysplat/.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"28 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673718","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}
The simulation of sand-water mixtures requires capturing the stochastic behavior of individual sand particles within a uniform, continuous fluid medium. However, most existing approaches, which only treat sand particles as markers within fluid solvers, fail to account for both the forces acting on individual sand particles and the collective feedback of the particle assemblies on the fluid. This prevents faithful reproduction of characteristic phenomena including transport, deposition, and clogging. Building upon kinetic ensemble averaging technique, we propose a physically consistent coupling strategy and introduce a novel Granule-In-Cell (GIC) method for modeling such sand-water interactions. We employ the Discrete Element Method (DEM) to capture fine-scale granule dynamics and the Particle-In-Cell (PIC) method for continuous spatial representation and density projection. To bridge these two frameworks, we treat granules as macroscopic transport flow rather than solid boundaries within the fluid domain. This bidirectional coupling allows our model to incorporate a range of interphase forces using different discretization schemes, resulting in more realistic simulations that strictly adhere to the mass conservation law. Experimental results demonstrate the effectiveness of our method in simulating complex sand-water interactions, uniquely capturing intricate physical phenomena and ensuring exact volume preservation compared to existing approaches.
{"title":"The Granule-In-Cell Method for Simulating Sand–Water Mixtures","authors":"Yizao Tang, Yuechen Zhu, Xingyu Ni, Baoquan Chen","doi":"10.1145/3763279","DOIUrl":"https://doi.org/10.1145/3763279","url":null,"abstract":"The simulation of sand-water mixtures requires capturing the stochastic behavior of individual sand particles within a uniform, continuous fluid medium. However, most existing approaches, which only treat sand particles as markers within fluid solvers, fail to account for both the forces acting on individual sand particles and the collective feedback of the particle assemblies on the fluid. This prevents faithful reproduction of characteristic phenomena including transport, deposition, and clogging. Building upon kinetic ensemble averaging technique, we propose a physically consistent coupling strategy and introduce a novel Granule-In-Cell (GIC) method for modeling such sand-water interactions. We employ the Discrete Element Method (DEM) to capture fine-scale granule dynamics and the Particle-In-Cell (PIC) method for continuous spatial representation and density projection. To bridge these two frameworks, we treat granules as macroscopic transport flow rather than solid boundaries within the fluid domain. This bidirectional coupling allows our model to incorporate a range of interphase forces using different discretization schemes, resulting in more realistic simulations that strictly adhere to the mass conservation law. Experimental results demonstrate the effectiveness of our method in simulating complex sand-water interactions, uniquely capturing intricate physical phenomena and ensuring exact volume preservation compared to existing approaches.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"367 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673721","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}
The intricate geometric complexity of knots, tangles, dreads and clumps require sophisticated grooming systems that allow artists to both realistically model and artistically control fur and hair systems. Recent volumetric and 3D neural style transfer techniques provided a new paradigm of art directability, allowing artists to modify assets drastically with the use of single style images. However, these previous 3D neural stylization approaches were limited to volumes and meshes. In this paper we propose the first stylization pipeline to support hair and fur. Through a carefully tailored fur/hair representation, our approach allows complex, 3D consistent and temporally coherent grooms that are stylized using style images.
{"title":"Shaping Strands with Neural Style Transfer","authors":"Beyzanur Coban, Pascal Chang, Guilherme Gomes Haetinger, Jingwei Tang, Vinicius C. Azevedo","doi":"10.1145/3763365","DOIUrl":"https://doi.org/10.1145/3763365","url":null,"abstract":"The intricate geometric complexity of knots, tangles, dreads and clumps require sophisticated grooming systems that allow artists to both realistically model and artistically control fur and hair systems. Recent volumetric and 3D neural style transfer techniques provided a new paradigm of art directability, allowing artists to modify assets drastically with the use of single style images. However, these previous 3D neural stylization approaches were limited to volumes and meshes. In this paper we propose the first stylization pipeline to support hair and fur. Through a carefully tailored fur/hair representation, our approach allows complex, 3D consistent and temporally coherent grooms that are stylized using style images.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"27 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145674171","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}
Prior research has demonstrated the efficacy of balanced trees as spatially adaptive grids for large-scale simulations. However, state-of-the-art methods for balanced tree construction are restricted by the iterative nature of the ripple effect, thus failing to fully leverage the massive parallelism offered by modern GPU architectures. We propose to reframe the construction of balanced trees as a process to merge N -balanced Minimum Spanning Trees ( N -balanced MSTs) generated from a collection of seed points. To ensure optimal performance, we propose a stack-free parallel strategy for constructing all internal nodes of a specified N -balanced MST. This approach leverages two 32-bit integer registers as buffers rather than relying on an integer array as a stack during construction, which helps maintain balanced workloads across different GPU threads. We then propose a dynamic update algorithm utilizing refinement counters for all internal nodes to enable parallel insertion and deletion operations of N -balanced MSTs. This design achieves significant efficiency improvements compared to full reconstruction from scratch, thereby facilitating fluid simulations in handling dynamic moving boundaries. Our approach is fully compatible with GPU implementation and demonstrates up to an order-of-magnitude speedup compared to the state-of-the-art method [Wang et al. 2024]. The source code for the paper is publicly available at https://github.com/peridyno/peridyno.
先前的研究已经证明了平衡树作为空间自适应网格在大规模模拟中的有效性。然而，最先进的平衡树构建方法受到涟漪效应迭代性质的限制，因此无法充分利用现代GPU架构提供的大规模并行性。我们建议将平衡树的构造重新定义为一个合并由种子点集合生成的N个平衡最小生成树（N -balanced MSTs）的过程。为了保证最优的性能，我们提出了一种无堆栈并行策略来构建指定N均衡MST的所有内部节点。这种方法利用两个32位整数寄存器作为缓冲区，而不是在构造期间依赖整数数组作为堆栈，这有助于在不同GPU线程之间保持平衡的工作负载。然后，我们提出了一种动态更新算法，利用所有内部节点的细化计数器来实现N平衡mst的并行插入和删除操作。与从头开始的完全重建相比，该设计实现了显著的效率提高，从而促进了处理动态移动边界的流体模拟。我们的方法与GPU实现完全兼容，并且与最先进的方法相比，速度提高了一个数量级[Wang et al. 2024]。该论文的源代码可在https://github.com/peridyno/peridyno上公开获取。
{"title":"A Stack-Free Parallel h-Adaptation Algorithm for Dynamically Balanced Trees on GPUs","authors":"Lixin Ren, Xiaowei He, Shusen Liu, Yuzhong Guo, Enhua Wu","doi":"10.1145/3763349","DOIUrl":"https://doi.org/10.1145/3763349","url":null,"abstract":"Prior research has demonstrated the efficacy of balanced trees as spatially adaptive grids for large-scale simulations. However, state-of-the-art methods for balanced tree construction are restricted by the iterative nature of the ripple effect, thus failing to fully leverage the massive parallelism offered by modern GPU architectures. We propose to reframe the construction of balanced trees as a process to merge <jats:italic toggle=\"yes\">N</jats:italic> -balanced Minimum Spanning Trees ( <jats:italic toggle=\"yes\">N</jats:italic> -balanced MSTs) generated from a collection of seed points. To ensure optimal performance, we propose a stack-free parallel strategy for constructing all internal nodes of a specified <jats:italic toggle=\"yes\">N</jats:italic> -balanced MST. This approach leverages two 32-bit integer registers as buffers rather than relying on an integer array as a stack during construction, which helps maintain balanced workloads across different GPU threads. We then propose a dynamic update algorithm utilizing refinement counters for all internal nodes to enable parallel insertion and deletion operations of <jats:italic toggle=\"yes\">N</jats:italic> -balanced MSTs. This design achieves significant efficiency improvements compared to full reconstruction from scratch, thereby facilitating fluid simulations in handling dynamic moving boundaries. Our approach is fully compatible with GPU implementation and demonstrates up to an order-of-magnitude speedup compared to the state-of-the-art method [Wang et al. 2024]. The source code for the paper is publicly available at https://github.com/peridyno/peridyno.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"115 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673768","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}
Kaixuan Wei, Hector Romero, Hadi Amata, Jipeng Sun, Qiang Fu, Felix Heide, Wolfgang Heidrich
Differentiable optics, as an emerging paradigm that jointly optimizes optics and (optional) image processing algorithms, has made many innovative optical designs possible across a broad range of imaging and display applications. Many of these systems utilize diffractive optical components for holography, PSF engineering, or wavefront shaping. Existing approaches have, however, mostly remained limited to laboratory prototypes, owing to a large quality gap between simulation and manufactured devices. We aim at lifting the fundamental technical barriers to the practical use of learned diffractive optical systems. To this end, we propose a fabrication-aware design pipeline for diffractive optics fabricated by direct-write grayscale lithography followed by replication with nano-imprinting, which is directly suited for inexpensive mass-production of large area designs. We propose a super-resolved neural lithography model that can accurately predict the 3D geometry generated by the fabrication process. This model can be seamlessly integrated into existing differentiable optics frameworks, enabling fabrication-aware, end-to-end optimization of computational optical systems. To tackle the computational challenges, we also devise tensor-parallel compute framework centered on distributing large-scale FFT computation across many GPUs. As such, we demonstrate large scale diffractive optics designs up to 32.16 mm × 21.44 mm, simulated on grids of up to 128,640 by 85,760 feature points. We find adequate agreement between simulation and fabricated prototypes for applications such as holography and PSF engineering. We also achieve high image quality from an imaging system comprised only of a single diffractive optical element, with images processed only by a one-step inverse filter utilizing the simulation PSF. We believe our findings lift the fabrication limitations for real-world applications of diffractive optics and differentiable optical design.
可微光学作为一种新兴的范例，共同优化光学和（可选）图像处理算法，使许多创新的光学设计在广泛的成像和显示应用中成为可能。许多这些系统利用衍射光学元件全息，PSF工程，或波前整形。然而，由于模拟和制造设备之间存在很大的质量差距，现有的方法大多仍然局限于实验室原型。我们的目标是解除基本的技术障碍，以实际使用的学习衍射光学系统。为此，我们提出了一种制造感知设计管道，用于通过直接写入灰度光刻制造的衍射光学器件，然后使用纳米压印复制，这直接适用于大面积设计的廉价批量生产。我们提出了一种超分辨神经光刻模型，可以准确地预测制造过程中产生的三维几何形状。该模型可以无缝集成到现有的可微分光学框架中，实现计算光学系统的制造感知端到端优化。为了解决计算挑战，我们还设计了张量并行计算框架，该框架以跨多个gpu分布大规模FFT计算为中心。因此，我们展示了32.16 mm × 21.44 mm的大规模衍射光学设计，在高达128,640 × 85,760个特征点的网格上进行了模拟。我们发现模拟和制造原型之间有足够的一致性，用于全息和PSF工程等应用。我们还通过仅由单个衍射光学元件组成的成像系统实现了高图像质量，图像仅通过利用模拟PSF的一步反滤波器处理。我们相信我们的发现解除了衍射光学和可微光学设计在实际应用中的制造限制。
{"title":"Large-Area Fabrication-aware Computational Diffractive Optics","authors":"Kaixuan Wei, Hector Romero, Hadi Amata, Jipeng Sun, Qiang Fu, Felix Heide, Wolfgang Heidrich","doi":"10.1145/3763358","DOIUrl":"https://doi.org/10.1145/3763358","url":null,"abstract":"Differentiable optics, as an emerging paradigm that jointly optimizes optics and (optional) image processing algorithms, has made many innovative optical designs possible across a broad range of imaging and display applications. Many of these systems utilize diffractive optical components for holography, PSF engineering, or wavefront shaping. Existing approaches have, however, mostly remained limited to laboratory prototypes, owing to a large quality gap between simulation and manufactured devices. We aim at lifting the fundamental technical barriers to the practical use of learned diffractive optical systems. To this end, we propose a fabrication-aware design pipeline for diffractive optics fabricated by direct-write grayscale lithography followed by replication with nano-imprinting, which is directly suited for inexpensive mass-production of large area designs. We propose a super-resolved neural lithography model that can accurately predict the 3D geometry generated by the fabrication process. This model can be seamlessly integrated into existing differentiable optics frameworks, enabling fabrication-aware, end-to-end optimization of computational optical systems. To tackle the computational challenges, we also devise tensor-parallel compute framework centered on distributing large-scale FFT computation across many GPUs. As such, we demonstrate large scale diffractive optics designs up to 32.16 mm × 21.44 mm, simulated on grids of up to 128,640 by 85,760 feature points. We find adequate agreement between simulation and fabricated prototypes for applications such as holography and PSF engineering. We also achieve high image quality from an imaging system comprised only of a single diffractive optical element, with images processed only by a one-step inverse filter utilizing the simulation PSF. We believe our findings lift the fabrication limitations for real-world applications of diffractive optics and differentiable optical design.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"21 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673775","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}
Kehan Xu, Benedikt Bitterli, Eugene d'Eon, Wojciech Jarosz
A fundamental challenge in rendering has been the dichotomy between surface and volume models. Gaussian Process Implicit Surfaces (GPISes) recently provided a unified approach for surfaces, volumes, and the spectrum in between. However, this representation remains impractical due to its high computational cost and mathematical complexity. We address these limitations by reformulating GPISes as procedural noise, eliminating expensive linear system solves while maintaining control over spatial correlations. Our method enables efficient sampling of stochastic realizations and supports flexible conditioning of values and derivatives through pathwise updates. To further enable practical rendering, we derive analytic distributions for surface normals, allowing for variance-reduced light transport via next-event estimation and multiple importance sampling. Our framework achieves efficient, high-quality rendering of stochastic surfaces and volumes with significantly simplified implementations on both CPU and GPU, while preserving the generality of the original GPIS representation.
{"title":"Practical Gaussian Process Implicit Surfaces with Sparse Convolutions","authors":"Kehan Xu, Benedikt Bitterli, Eugene d'Eon, Wojciech Jarosz","doi":"10.1145/3763329","DOIUrl":"https://doi.org/10.1145/3763329","url":null,"abstract":"A fundamental challenge in rendering has been the dichotomy between surface and volume models. Gaussian Process Implicit Surfaces (GPISes) recently provided a unified approach for surfaces, volumes, and the spectrum in between. However, this representation remains impractical due to its high computational cost and mathematical complexity. We address these limitations by reformulating GPISes as procedural noise, eliminating expensive linear system solves while maintaining control over spatial correlations. Our method enables efficient sampling of stochastic realizations and supports flexible conditioning of values and derivatives through pathwise updates. To further enable practical rendering, we derive analytic distributions for surface normals, allowing for variance-reduced light transport via next-event estimation and multiple importance sampling. Our framework achieves efficient, high-quality rendering of stochastic surfaces and volumes with significantly simplified implementations on both CPU and GPU, while preserving the generality of the original GPIS representation.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"1 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673856","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}
Jose Luis Ponton, Sheldon Andrews, Carlos Andujar, Nuria Pelechano
Interactive applications demand believable characters that respond naturally to dynamic environments. Traditional character animation techniques often struggle to handle arbitrary situations, leading to a growing trend of dynamically selecting motion-captured animations based on predefined features. While Motion Matching has proven effective for locomotion by aligning to target trajectories, animating environment interactions and crowd behaviors remains challenging due to the need to consider surrounding elements. Existing approaches often involve manual setup or lack the naturalism of motion capture. Furthermore, in crowd animation, body animation is frequently treated as a separate process from trajectory planning, leading to inconsistencies between body pose and root motion. To address these limitations, we present Environment-aware Motion Matching , a novel real-time system for full-body character animation that dynamically adapts to obstacles and other agents, emphasizing the bidirectional relationship between pose and trajectory. In a preprocessing step, we extract shape, pose, and trajectory features from a motion capture database. At runtime, we perform an efficient search that matches user input and current pose while penalizing collisions with a dynamic environment. Our method allows characters to naturally adjust their pose and trajectory to navigate crowded scenes.
{"title":"Environment-aware Motion Matching","authors":"Jose Luis Ponton, Sheldon Andrews, Carlos Andujar, Nuria Pelechano","doi":"10.1145/3763334","DOIUrl":"https://doi.org/10.1145/3763334","url":null,"abstract":"Interactive applications demand believable characters that respond naturally to dynamic environments. Traditional character animation techniques often struggle to handle arbitrary situations, leading to a growing trend of dynamically selecting motion-captured animations based on predefined features. While Motion Matching has proven effective for locomotion by aligning to target trajectories, animating environment interactions and crowd behaviors remains challenging due to the need to consider surrounding elements. Existing approaches often involve manual setup or lack the naturalism of motion capture. Furthermore, in crowd animation, body animation is frequently treated as a separate process from trajectory planning, leading to inconsistencies between body pose and root motion. To address these limitations, we present <jats:italic toggle=\"yes\">Environment-aware Motion Matching</jats:italic> , a novel real-time system for full-body character animation that dynamically adapts to obstacles and other agents, emphasizing the bidirectional relationship between pose and trajectory. In a preprocessing step, we extract shape, pose, and trajectory features from a motion capture database. At runtime, we perform an efficient search that matches user input and current pose while penalizing collisions with a dynamic environment. Our method allows characters to naturally adjust their pose and trajectory to navigate crowded scenes.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"155 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673921","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}
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