Shaojie Bai, Seunghyeon Seo, Yida Wang, Chenghui Li, Owen Wang, Te-Li Wang, Tianyang Ma, Jason Saragih, Shih-En Wei, Nojun Kwak, Hyung Jun(John) Kim
Enabling photorealistic avatar animations in virtual and augmented reality (VR/AR) has been challenging because of the difficulty of obtaining ground truth state of faces. It is physically impossible to obtain synchronized images from head-mounted cameras (HMC) sensing input, which has partial observations in infrared (IR), and an array of outside-in dome cameras, which have full observations that match avatars' appearance. Prior works relying on analysis-by-synthesis methods could generate accurate ground truth, but suffer from imperfect disentanglement between expression and style in their personalized training. The reliance of extensive paired captures (HMC and dome) for the same subject makes it operationally expensive to collect large-scale datasets, which cannot be reused for different HMC viewpoints and lighting. In this work, we propose a novel generative approach, Generative HMC (GenHMC), that leverages large unpaired HMC captures , which are much easier to collect, to directly generate high-quality synthetic HMC images given any conditioning avatar state from dome captures. We show that our method is able to properly disentangle the input conditioning signal that specifies facial expression and viewpoint, from facial appearance, leading to more accurate ground truth. Furthermore, our method can generalize to unseen identities, removing the reliance on the paired captures. We demonstrate these breakthroughs by both evaluating synthetic HMC images and universal face encoders trained from these new HMC-avatar correspondences, which achieve better data efficiency and state-of-the-art accuracy.
{"title":"Generative Head-Mounted Camera Captures for Photorealistic Avatars","authors":"Shaojie Bai, Seunghyeon Seo, Yida Wang, Chenghui Li, Owen Wang, Te-Li Wang, Tianyang Ma, Jason Saragih, Shih-En Wei, Nojun Kwak, Hyung Jun(John) Kim","doi":"10.1145/3763300","DOIUrl":"https://doi.org/10.1145/3763300","url":null,"abstract":"Enabling photorealistic avatar animations in virtual and augmented reality (VR/AR) has been challenging because of the difficulty of obtaining ground truth state of faces. It is <jats:italic toggle=\"yes\">physically impossible</jats:italic> to obtain synchronized images from head-mounted cameras (HMC) sensing input, which has partial observations in infrared (IR), and an array of outside-in dome cameras, which have full observations that match avatars' appearance. Prior works relying on analysis-by-synthesis methods could generate accurate ground truth, but suffer from imperfect disentanglement between expression and style in their personalized training. The reliance of extensive paired captures (HMC and dome) for the <jats:italic toggle=\"yes\">same</jats:italic> subject makes it operationally expensive to collect large-scale datasets, which cannot be reused for different HMC viewpoints and lighting. In this work, we propose a novel generative approach, Generative HMC (GenHMC), that leverages <jats:italic toggle=\"yes\">large unpaired HMC captures</jats:italic> , which are much easier to collect, to directly generate high-quality <jats:italic toggle=\"yes\">synthetic</jats:italic> HMC images given any conditioning avatar state from dome captures. We show that our method is able to properly disentangle the input conditioning signal that specifies facial expression and viewpoint, from facial appearance, leading to more accurate ground truth. Furthermore, our method can generalize to unseen identities, removing the reliance on the paired captures. We demonstrate these breakthroughs by both evaluating synthetic HMC images and universal face encoders trained from these new HMC-avatar correspondences, which achieve better data efficiency and state-of-the-art accuracy.","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":"145673922","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}
We present a novel combustion simulation framework to model fire phenomena across solids, liquids, and gases. Our approach extends traditional fluid solvers by incorporating multi-species thermodynamics and reactive transport for fuel, oxygen, nitrogen, carbon dioxide, water vapor, and residuals. Combustion reactions are governed by stoichiometry-dependent heat release, allowing an accurate simulation of premixed and diffusive flames with varying intensity and composition. We support a wide range of scenarios including jet fires, water suppression (sprays and sprinklers), fuel evaporation, and starvation conditions. Our framework enables interactive heat sources, fire detectors, and realistic rendering of flames (e.g., laminar-to-turbulent transitions and blue-to-orange color shifts). Our key contributions include the tight coupling of species dynamics with thermodynamic feedback, evaporation modeling, and a hybrid SPH-grid representation for the efficient simulation of extinguishing fires. We validate our method through numerous experiments that demonstrate its versatility in both indoor and outdoor fire scenarios.
{"title":"Fire-X: Extinguishing Fire with Stoichiometric Heat Release","authors":"Helge Wrede, Anton Wagner, Sarker Miraz Mahfuz, Wojtek Palubicki, Dominik Michels, Sören Pirk","doi":"10.1145/3763338","DOIUrl":"https://doi.org/10.1145/3763338","url":null,"abstract":"We present a novel combustion simulation framework to model fire phenomena across solids, liquids, and gases. Our approach extends traditional fluid solvers by incorporating multi-species thermodynamics and reactive transport for fuel, oxygen, nitrogen, carbon dioxide, water vapor, and residuals. Combustion reactions are governed by stoichiometry-dependent heat release, allowing an accurate simulation of premixed and diffusive flames with varying intensity and composition. We support a wide range of scenarios including jet fires, water suppression (sprays and sprinklers), fuel evaporation, and starvation conditions. Our framework enables interactive heat sources, fire detectors, and realistic rendering of flames (e.g., laminar-to-turbulent transitions and blue-to-orange color shifts). Our key contributions include the tight coupling of species dynamics with thermodynamic feedback, evaporation modeling, and a hybrid SPH-grid representation for the efficient simulation of extinguishing fires. We validate our method through numerous experiments that demonstrate its versatility in both indoor and outdoor fire scenarios.","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":"145673931","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}
This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representation through differentiable rendering techniques. Therefore, they heavily rely on the quality of video segmentation maps, which are often unstable, leading to unreliable reconstruction results. To overcome this challenge, our key idea is to represent the decomposed 4D scene with the Freetime FeatureGS and design a streaming feature learning strategy to accurately recover it from per-image segmentation maps, eliminating the need for video segmentation. Freetime FeatureGS models the dynamic scene as a set of Gaussian primitives with learnable features and linear motion ability, allowing them to move to neighboring regions over time. We apply a contrastive loss to Freetime FeatureGS, forcing primitive features to be close or far apart based on whether their projections belong to the same instance in the 2D segmentation map. As our Gaussian primitives can move across time, it naturally extends the feature learning to the temporal dimension, achieving 4D segmentation. Furthermore, we sample observations for training in a temporally ordered manner, enabling the streaming propagation of features over time and effectively avoiding local minima during the optimization process. Experimental results on several datasets show that the reconstruction quality of our method outperforms recent methods by a large margin.
{"title":"Split4D: Decomposed 4D Scene Reconstruction Without Video Segmentation","authors":"Yongzhen Hu, Yihui Yang, Haotong Lin, Yifan Wang, Junting Dong, Yifu Deng, Xinyu Zhu, Fan Jia, Hujun Bao, Xiaowei Zhou, Sida Peng","doi":"10.1145/3763343","DOIUrl":"https://doi.org/10.1145/3763343","url":null,"abstract":"This paper addresses the problem of decomposed 4D scene reconstruction from multi-view videos. Recent methods achieve this by lifting video segmentation results to a 4D representation through differentiable rendering techniques. Therefore, they heavily rely on the quality of video segmentation maps, which are often unstable, leading to unreliable reconstruction results. To overcome this challenge, our key idea is to represent the decomposed 4D scene with the Freetime FeatureGS and design a streaming feature learning strategy to accurately recover it from per-image segmentation maps, eliminating the need for video segmentation. Freetime FeatureGS models the dynamic scene as a set of Gaussian primitives with learnable features and linear motion ability, allowing them to move to neighboring regions over time. We apply a contrastive loss to Freetime FeatureGS, forcing primitive features to be close or far apart based on whether their projections belong to the same instance in the 2D segmentation map. As our Gaussian primitives can move across time, it naturally extends the feature learning to the temporal dimension, achieving 4D segmentation. Furthermore, we sample observations for training in a temporally ordered manner, enabling the streaming propagation of features over time and effectively avoiding local minima during the optimization process. Experimental results on several datasets show that the reconstruction quality of our method outperforms recent methods by a large margin.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"4 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673774","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}
We present a novel method for accurately calibrating the optical properties of full-color 3D printers using only a single, directly printable calibration target. Our approach is based on accurate multiple-scattering light transport and estimates the single-scattering albedo and extinction coefficient for each resin. These parameters are essential for both soft-proof rendering of 3D printouts and for advanced, scattering-aware 3D halftoning algorithms. In contrast to previous methods that rely on thin, precisely fabricated resin samples and labor-intensive manual processing, our technique achieves higher accuracy with significantly less effort. Our calibration target is specifically designed to enable algorithmic recovery of each resin's optical properties through a series of one-dimensional and two-dimensional numerical optimizations, applied first on the white and black resins, and then on any remaining resins. The method supports both RGB and spectral calibration, depending on whether a camera or spectrometer is used to capture the calibration target. It also scales linearly with the number of resins, making it well-suited for modern multi-material printers. We validate our approach extensively, first on synthetic and then on real resins across 242 color mixtures, printed thin translucent samples, printed surface textures, and fully textured 3D models with complex geometry, including an eye model and a figurine.
{"title":"Scattering-Aware Color Calibration for 3D Printers Using a Simple Calibration Target","authors":"Tomáš Iser, Tobias Rittig, Alexander Wilkie","doi":"10.1145/3763293","DOIUrl":"https://doi.org/10.1145/3763293","url":null,"abstract":"We present a novel method for accurately calibrating the optical properties of full-color 3D printers using only a single, directly printable calibration target. Our approach is based on accurate multiple-scattering light transport and estimates the single-scattering albedo and extinction coefficient for each resin. These parameters are essential for both soft-proof rendering of 3D printouts and for advanced, scattering-aware 3D halftoning algorithms. In contrast to previous methods that rely on thin, precisely fabricated resin samples and labor-intensive manual processing, our technique achieves higher accuracy with significantly less effort. Our calibration target is specifically designed to enable algorithmic recovery of each resin's optical properties through a series of one-dimensional and two-dimensional numerical optimizations, applied first on the white and black resins, and then on any remaining resins. The method supports both RGB and spectral calibration, depending on whether a camera or spectrometer is used to capture the calibration target. It also scales linearly with the number of resins, making it well-suited for modern multi-material printers. We validate our approach extensively, first on synthetic and then on real resins across 242 color mixtures, printed thin translucent samples, printed surface textures, and fully textured 3D models with complex geometry, including an eye model and a figurine.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"5 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673719","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}
Remanufacturing effectively extends component lifespans by restoring used or end-of-life parts to like-new or even superior conditions, with an emphasis on maximizing reutilized material, especially for high-cost materials. Hybrid manufacturing technology combines the capabilities of additive and subtractive manufacturing, with the ability to add and remove material, enabling it to remanufacture complex shapes and is increasingly being applied in remanufacturing. How to effectively plan the process of additive and subtractive hybrid remanufacturing (ASHRM) to maximize material reutilization has become a key focus of attention. However, current ASHRM process planning methods lack strict consideration of collision-free constraints, hindering practical application. This paper introduces a computational framework to tackle ASHRM process planning for general shapes with strictly considering these constraints. We separate global and local collision-free constraints, employing clipping planes and graph to tackle them respectively, ultimately maximizing the reutilized volume while ensuring these constraints are satisfied. Additionally, we also optimize the setup of the target model that is conducive to maximizing the reutilized volume. Extensive experiments and physical validations on a 5-axis hybrid manufacturing platform demonstrate the effectiveness of our method across various 3D shapes, achieving an average material reutilization of 69% across 12 cases. Code is publicly available at https://github.com/fanchao98/Waste-to-Value.
{"title":"Waste-to-Value: Reutilized Material Maximization for Additive and Subtractive Hybrid Remanufacturing","authors":"Fanchao Zhong, Zhenmin Zhang, Liyuan Wang, Xin Yan, Jikai Liu, Lin Lu, Haisen Zhao","doi":"10.1145/3763313","DOIUrl":"https://doi.org/10.1145/3763313","url":null,"abstract":"Remanufacturing effectively extends component lifespans by restoring used or end-of-life parts to like-new or even superior conditions, with an emphasis on maximizing reutilized material, especially for high-cost materials. Hybrid manufacturing technology combines the capabilities of additive and subtractive manufacturing, with the ability to add and remove material, enabling it to remanufacture complex shapes and is increasingly being applied in remanufacturing. How to effectively plan the process of additive and subtractive hybrid remanufacturing (ASHRM) to maximize material reutilization has become a key focus of attention. However, current ASHRM process planning methods lack strict consideration of collision-free constraints, hindering practical application. This paper introduces a computational framework to tackle ASHRM process planning for general shapes with strictly considering these constraints. We separate global and local collision-free constraints, employing clipping planes and graph to tackle them respectively, ultimately maximizing the reutilized volume while ensuring these constraints are satisfied. Additionally, we also optimize the setup of the target model that is conducive to maximizing the reutilized volume. Extensive experiments and physical validations on a 5-axis hybrid manufacturing platform demonstrate the effectiveness of our method across various 3D shapes, achieving an average material reutilization of 69% across 12 cases. Code is publicly available at https://github.com/fanchao98/Waste-to-Value.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"125 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673722","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}
Estimating lighting in indoor scenes is particularly challenging due to diverse distribution of light sources and complexity of scene geometry. Previous methods mainly focused on spatial variability and consistency for a single image or temporal consistency for video sequences. However, these approaches fail to achieve spatio-temporal consistency in video lighting estimation, which restricts applications such as compositing animated models into videos. In this paper, we propose STGlight, a lightweight and effective method for spatio-temporally consistent video lighting estimation, where our network processes a stream of LDR RGB-D video frames while maintaining incrementally updated global representations of both geometry and lighting, enabling the prediction of HDR environment maps at arbitrary locations for each frame. We model indoor lighting with three components: visible light sources providing direct illumination, ambient lighting approximating indirect illumination, and local environment textures producing high-quality specular reflections on glossy objects. To capture spatial-varying lighting, we represent scene geometry with point clouds, which support efficient spatio-temporal fusion and allow us to handle moderately dynamic scenes. To ensure temporal consistency, we apply a transformer-based fusion block that propagates lighting features across frames. Building on this, we further handle dynamic lighting with moving objects or changing light conditions by applying intrinsic decomposition on the point cloud and integrating the decomposed components with a neural fusion module. Experiments show that our online method can effectively predict lighting for any position within the video stream, while maintaining spatial variability and spatio-temporal consistency. Code is available at: https://github.com/nauyihsnehs/STGlight.
{"title":"STGlight: Online Indoor Lighting Estimation via Spatio-Temporal Gaussian Fusion","authors":"Shiyuan Shen, Zhongyun Bao, Hong Ding, Wenju Xu, Tenghui Lai, Chunxia Xiao","doi":"10.1145/3763350","DOIUrl":"https://doi.org/10.1145/3763350","url":null,"abstract":"Estimating lighting in indoor scenes is particularly challenging due to diverse distribution of light sources and complexity of scene geometry. Previous methods mainly focused on spatial variability and consistency for a single image or temporal consistency for video sequences. However, these approaches fail to achieve spatio-temporal consistency in video lighting estimation, which restricts applications such as compositing animated models into videos. In this paper, we propose STGlight, a lightweight and effective method for spatio-temporally consistent video lighting estimation, where our network processes a stream of LDR RGB-D video frames while maintaining incrementally updated global representations of both geometry and lighting, enabling the prediction of HDR environment maps at arbitrary locations for each frame. We model indoor lighting with three components: visible light sources providing direct illumination, ambient lighting approximating indirect illumination, and local environment textures producing high-quality specular reflections on glossy objects. To capture spatial-varying lighting, we represent scene geometry with point clouds, which support efficient spatio-temporal fusion and allow us to handle moderately dynamic scenes. To ensure temporal consistency, we apply a transformer-based fusion block that propagates lighting features across frames. Building on this, we further handle dynamic lighting with moving objects or changing light conditions by applying intrinsic decomposition on the point cloud and integrating the decomposed components with a neural fusion module. Experiments show that our online method can effectively predict lighting for any position within the video stream, while maintaining spatial variability and spatio-temporal consistency. Code is available at: https://github.com/nauyihsnehs/STGlight.","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":"145673866","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}
Jiwei Wang, Wenbin Song, Yicheng Fan, Yang Wang, Xiaopei Liu
Unmanned aerial vehicles (UAVs) have demonstrated remarkable efficacy across diverse fields. Nevertheless, developing flight controllers tailored to a specific UAV design, particularly in environments with strong fluid-interactive dynamics, remains challenging. Conventional controller design experiences often fall short in such cases, rendering it infeasible to apply time-tested practices. Consequently, a simulation test bed becomes indispensable for controller design and evaluation prior to its actual implementation on the physical UAV. This platform should allow for meticulous adjustment of controllers and should be able to transfer to real-world systems without significant performance degradation. Existing simulators predominantly hinge on empirical models due to high efficiency, often overlooking the dynamic interplay between the UAV and the surrounding airflow. This makes it difficult to mimic more complex flight maneuvers, such as an abrupt midair halt inside narrow channels, in which the UAV may experience strong fluid-structure interactions. On the other hand, simulators considering the complex surrounding airflow are extremely slow and inadequate to support the design and evaluation of flight controllers. In this paper, we present a novel remedy for highly-efficient UAV flight simulations, which entails a hybrid modeling that deftly combines our novel far-field adaptive block-based fluid simulator with parametric empirical models situated near the boundary of the UAV, with the model parameters automatically calibrated. With this newly devised simulator, a broader spectrum of flight scenarios can be explored for controller design and assessment, encompassing those influenced by potent close-proximity effects, or situations where multiple UAVs operate in close quarters. The practical worth of our simulator has been authenticated through comparisons with actual UAV flight data. We further showcase its utility in designing flight controllers for fixed-wing, multi-rotor, and hybrid UAVs, and even exemplify its application when multiple UAVs are involved, underlining the unique value of our system for flight controllers.
{"title":"A Highly-Efficient Hybrid Simulation System for Flight Controller Design and Evaluation of Unmanned Aerial Vehicles","authors":"Jiwei Wang, Wenbin Song, Yicheng Fan, Yang Wang, Xiaopei Liu","doi":"10.1145/3763283","DOIUrl":"https://doi.org/10.1145/3763283","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have demonstrated remarkable efficacy across diverse fields. Nevertheless, developing flight controllers tailored to a specific UAV design, particularly in environments with strong fluid-interactive dynamics, remains challenging. Conventional controller design experiences often fall short in such cases, rendering it infeasible to apply time-tested practices. Consequently, a simulation test bed becomes indispensable for controller design and evaluation prior to its actual implementation on the physical UAV. This platform should allow for meticulous adjustment of controllers and should be able to transfer to real-world systems without significant performance degradation. Existing simulators predominantly hinge on empirical models due to high efficiency, often overlooking the dynamic interplay between the UAV and the surrounding airflow. This makes it difficult to mimic more complex flight maneuvers, such as an abrupt midair halt inside narrow channels, in which the UAV may experience strong fluid-structure interactions. On the other hand, simulators considering the complex surrounding airflow are extremely slow and inadequate to support the design and evaluation of flight controllers. In this paper, we present a novel remedy for highly-efficient UAV flight simulations, which entails a hybrid modeling that deftly combines our novel far-field adaptive block-based fluid simulator with parametric empirical models situated near the boundary of the UAV, with the model parameters automatically calibrated. With this newly devised simulator, a broader spectrum of flight scenarios can be explored for controller design and assessment, encompassing those influenced by potent close-proximity effects, or situations where multiple UAVs operate in close quarters. The practical worth of our simulator has been authenticated through comparisons with actual UAV flight data. We further showcase its utility in designing flight controllers for fixed-wing, multi-rotor, and hybrid UAVs, and even exemplify its application when multiple UAVs are involved, underlining the unique value of our system for flight controllers.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"34 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673925","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}
Tianze Guo, Zhedong Chen, Yi Jiang, Linjun Wu, Xilei Wei, Lang Xu, Yeshuang Lin, He Wang, Xiaogang Jin
Geometry-aware online motion retargeting is crucial for real-time character animation in gaming and virtual reality. However, existing methods often rely on complex optimization procedures or deep neural networks, which constrain their applicability in real-time scenarios. Moreover, they offer limited control over fine-grained motion details involved in character interactions, resulting in less realistic outcomes. To overcome these limitations, we propose a novel optimization framework for ultrafast, lightweight motion retargeting with joint-level control (i.e., controls over joint position, bone orientation, etc,). Our approach introduces a semantic-aware objective grounded in a spherical geometry representation, coupled with a bone-length-preserving algorithm that iteratively solves this objective. This formulation preserves spatial relationships among spheres, thereby maintaining motion semantics, mitigating interpenetration, and ensuring contact. It is lightweight and computationally efficient, making it particularly suitable for time-critical real-time deployment scenarios. Additionally, we incorporate a heuristic optimization strategy that enables rapid convergence and precise joint-level control. We evaluate our method against state-of-the-art approaches on the Mixamo dataset, and experimental results demonstrate that it achieves comparable performance while delivering an order-of-magnitude speedup.
{"title":"Ultrafast and Controllable Online Motion Retargeting for Game Scenarios","authors":"Tianze Guo, Zhedong Chen, Yi Jiang, Linjun Wu, Xilei Wei, Lang Xu, Yeshuang Lin, He Wang, Xiaogang Jin","doi":"10.1145/3763351","DOIUrl":"https://doi.org/10.1145/3763351","url":null,"abstract":"Geometry-aware online motion retargeting is crucial for real-time character animation in gaming and virtual reality. However, existing methods often rely on complex optimization procedures or deep neural networks, which constrain their applicability in real-time scenarios. Moreover, they offer limited control over fine-grained motion details involved in character interactions, resulting in less realistic outcomes. To overcome these limitations, we propose a novel optimization framework for ultrafast, lightweight motion retargeting with joint-level control (i.e., controls over joint position, bone orientation, etc,). Our approach introduces a semantic-aware objective grounded in a spherical geometry representation, coupled with a bone-length-preserving algorithm that iteratively solves this objective. This formulation preserves spatial relationships among spheres, thereby maintaining motion semantics, mitigating interpenetration, and ensuring contact. It is lightweight and computationally efficient, making it particularly suitable for time-critical real-time deployment scenarios. Additionally, we incorporate a heuristic optimization strategy that enables rapid convergence and precise joint-level control. We evaluate our method against state-of-the-art approaches on the Mixamo dataset, and experimental results demonstrate that it achieves comparable performance while delivering an order-of-magnitude speedup.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"10 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673926","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}
Integral linear operators play a key role in many graphics problems, but solutions obtained via Monte Carlo methods often suffer from high variance. A common strategy to improve the efficiency of integration across various inputs is to precompute the kernel function. Traditional methods typically rely on basis expansions for both the input and output functions. However, using fixed output bases can restrict the precision of output reconstruction and limit the compactness of the kernel representation. In this work, we introduce a new method that approximates both the kernel and the input function using Gaussian mixtures. This formulation allows the integral operator to be evaluated analytically, leading to improved flexibility in kernel storage and output representation. Moreover, our method naturally supports the sequential application of multiple operators and enables closed-form operator composition, which is particularly beneficial in tasks involving chains of operators. We demonstrate the versatility and effectiveness of our approach across a variety of graphics problems, including environment map relighting, boundary value problems, and fluorescence rendering.
{"title":"Gaussian Integral Linear Operators for Precomputed Graphics","authors":"Haolin Lu, Yash Belhe, Gurprit Singh, Tzu-Mao Li, Toshiya Hachisuka","doi":"10.1145/3763321","DOIUrl":"https://doi.org/10.1145/3763321","url":null,"abstract":"Integral linear operators play a key role in many graphics problems, but solutions obtained via Monte Carlo methods often suffer from high variance. A common strategy to improve the efficiency of integration across various inputs is to precompute the kernel function. Traditional methods typically rely on basis expansions for both the input and output functions. However, using fixed output bases can restrict the precision of output reconstruction and limit the compactness of the kernel representation. In this work, we introduce a new method that approximates both the kernel and the input function using Gaussian mixtures. This formulation allows the integral operator to be evaluated analytically, leading to improved flexibility in kernel storage and output representation. Moreover, our method naturally supports the sequential application of multiple operators and enables closed-form operator composition, which is particularly beneficial in tasks involving chains of operators. We demonstrate the versatility and effectiveness of our approach across a variety of graphics problems, including environment map relighting, boundary value problems, and fluorescence rendering.","PeriodicalId":50913,"journal":{"name":"ACM Transactions on Graphics","volume":"34 1","pages":""},"PeriodicalIF":6.2,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673933","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}
When observing an intense light source, humans perceive dense radiating spikes known as glare/starburst patterns. These patterns are frequently used in computer graphics applications to enhance the perception of brightness (e.g., in games and films). Previous works have computed the physical energy distribution of glare patterns under daytime conditions using approximations like Fresnel diffraction. These techniques are capable of producing visually believable results, particularly when the pupil remains small. However, they are insufficient under nighttime conditions, when the pupil is significantly dilated and the assumptions behind the approximations no longer hold. To address this, we employ the Rayleigh-Sommerfeld diffraction solution, from which Fresnel diffraction is derived as an approximation, as our baseline reference. In pursuit of performance and visual quality, we also employ Ochoa's approximation and the Chirp Z transform to efficiently generate high-resolution results for computer graphics applications. By also taking into account background illumination and certain physiological characteristics of the human photoreceptor cells, particularly the visual threshold of light stimulus, we propose a framework capable of producing plausible visual depictions of glare patterns for both daytime and nighttime scenes.
{"title":"Glare Pattern Depiction: High-Fidelity Physical Computation and Physiologically-Inspired Visual Response","authors":"Yuxiang Sun, Gladimir V. G. Baranoski","doi":"10.1145/3763356","DOIUrl":"https://doi.org/10.1145/3763356","url":null,"abstract":"When observing an intense light source, humans perceive dense radiating spikes known as glare/starburst patterns. These patterns are frequently used in computer graphics applications to enhance the perception of brightness (e.g., in games and films). Previous works have computed the physical energy distribution of glare patterns under daytime conditions using approximations like Fresnel diffraction. These techniques are capable of producing visually believable results, particularly when the pupil remains small. However, they are insufficient under nighttime conditions, when the pupil is significantly dilated and the assumptions behind the approximations no longer hold. To address this, we employ the Rayleigh-Sommerfeld diffraction solution, from which Fresnel diffraction is derived as an approximation, as our baseline reference. In pursuit of performance and visual quality, we also employ Ochoa's approximation and the Chirp Z transform to efficiently generate high-resolution results for computer graphics applications. By also taking into account background illumination and certain physiological characteristics of the human photoreceptor cells, particularly the visual threshold of light stimulus, we propose a framework capable of producing plausible visual depictions of glare patterns for both daytime and nighttime 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":"145673855","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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