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This study elucidates the determining role of initial recrystallization degree on grain growth tendency during the brazing process (heat treated at 1100 °C and held for 15 min) of high‐Nb 444‐type ferritic stainless steel used in automotive exhaust systems. The incomplete recrystallized grains induced by insufficient annealing of cold rolled stainless steel plate are more likely to promote abnormal grain growth in the actual brazing process. The simulation experiment results of grain growth tendency show that a matrix with 3.7% deformed grains and nonuniform grain size causes abnormal grain growth during reheating, and the starting temperature of grain coarsening decreases from 1100 to 1000 °C. During reheating, the deformed grains induces a rapid precipitation of Nb‐rich particles at low temperature, and the volume fraction is higher than that of the sample with more completed recrystallization. In addition, the study also finds that an increase in Nb content can increase the precipitation temperature of Laves phase, induce the formation of Laves phase distributed along ferrite grain boundaries in high‐Nb sample during brazing, and inhibit the coarsening of ferrite grains. [ABSTRACT FROM AUTHOR]

Visual querying of spatiotemporal data has become a dominant mode in the field of visual analytics. Previous studies have utilized well‐designed data structures to speed up the querying of spatiotemporal data. However, reducing storage overhead while improving the querying efficiency of data distribution remains a significant challenge. We propose a flow‐based neural representation method for efficient visual querying. First, we transform spatiotemporal data into density maps through kernel density estimation. Then, we leverage the data‐driven modeling capabilities of a flow‐based neural network to achieve a highly latent representation of the data. Various computations and queries can be performed on the latent representation to improve querying efficiency. Our experiments demonstrate that our approach achieves competitive results in visually querying spatiotemporal data in terms of storage overhead and real‐time interaction efficiency. [ABSTRACT FROM AUTHOR]

Road network design, as an important part of landscape modeling, shows a great significance in automatic driving, video game development, and disaster simulation. To date, this task remains labor‐intensive, tedious and time‐consuming. Many improved techniques have been proposed during the last two decades. Nevertheless, most of the state‐of‐the‐art methods still encounter problems of intuitiveness, usefulness and/or interactivity. As a rapid deviation from the conventional road design, this paper advocates an improved road modeling framework for automatic and interactive road production driven by geographical maps (including elevation, water, vegetation maps). Our method integrates the capability of flexible image generation models with powerful transformer architecture to afford a vectorized road network. We firstly construct a dataset that includes road graphs, density map and their corresponding geographical maps. Secondly, we develop a density map generation network based on image translation model with an attention mechanism to predict a road density map. The usage of density map facilitates faster convergence and better performance, which also serves as the input for road graph generation. Thirdly, we employ the transformer architecture to evolve density maps to road graphs. Our comprehensive experimental results have verified the efficiency, robustness and applicability of our newly‐proposed framework for road design. [ABSTRACT FROM AUTHOR]

Desiccation cracking of soil‐like materials is a common phenomenon in natural dry environment, however, it remains a challenge to model and simulate complicated multi‐physical processes inside the porous structure. With the goal of tracking such physical evolution accurately, we propose an MPM based method to simulate volumetric shrinkage and crack during moisture diffusion. At the physical level, we introduce Richards equations to evolve the dynamic moisture field to model evaporation and diffusion in unsaturated soils, with which a elastoplastic model is established to simulate strength changes and volumetric shrinkage via a novel saturation‐based hardening strategy during plastic treatment. At the algorithmic level, we develop an MPM‐fashion numerical solver for the proposed physical model and achieve stable yet efficient simulation towards delicate deformation and fracture. At the geometric level, we propose a correlating stretching criteria and a saturation‐aware extrapolation scheme to extend existing surface reconstruction for MPM, producing visual compelling soil appearance. Finally, we manifest realistic simulation results based on the proposed method with several challenging scenarios, which demonstrates usability and efficiency of our method. [ABSTRACT FROM AUTHOR]

The role of the leader in the self‐organizing crowd has a significant influence on the crowd evacuation when an emergency occurs. However, current crowd evacuation models either ignore the leadership characteristics of pedestrians or predefine an invariable leader identity. This article clearly distinguishes leaders from followers in a crowd and evaluates the impact of the leader's role on crowd evacuation under dynamic situations. First, the leadership model of the pedestrian is built based on the classic OCEAN personality parameters. Social dominance tendency is measured for more credibility. Then, the dynamic leader role model (DLRM) is proposed to describe the relationship between leaders and followers, considering the interaction among pedestrians during the evacuation. Finally, a novel crowd simulation algorithm based on the above DLRM is presented using the extended social force model. Various experiments in several typical scenarios verified that our proposed simulation model has better realism. [ABSTRACT FROM AUTHOR]

Realistic crowd simulation has always been an important research field in computer graphics. While both agent‐based motion models and data‐driven behavior models have made some progress, they are still suffering from either huge effort of multi‐parameter tuning or limited realistic motion. In this article, we propose a novel and differentiable multi‐parameter learning method for crowd simulation, which is called ORCANet. The main idea is to learn from real data and inverse evaluating the multi‐parameter for subsequent simulation. ORCANet uses classic optimal reciprocal collision avoidance (ORCA) as a basic motion model which is integrated into the deep learning framework. Addressing the feature of linear programming and non‐differentiable operation, a Gaussian kernel is added to approximate the role of neighbor distance in collision avoidance, which turns the original discrete operation into a fully differentiable forward simulation. Furthermore, we leverage ORCANet to optimize the multi‐parameter combination in synthetic and real‐world datasets. ORCANet is proved to rapidly converge to correct parameter values and regenerate the input synthetic sequence. Moreover, experiments on real‐world datasets by the metric of pedestrian trajectories verified that a more realistic crowd simulation has been generated through ORCANet. [ABSTRACT FROM AUTHOR]

Maritime shipping is under increasing pressure to alleviate its environmental impact. To this end, life‐cycle footprint accounting provides a foundation for taking targeted measures in the green transition of shipping. This study used the process‐based hybrid life‐cycle inventory (LCI) modeling approach to estimate the "cradle‐to‐propeller" footprint of ships, including primary energy consumption, carbon dioxide emissions, and sulfur dioxide emissions. We used the input–output LCI model to calculate the embodied energy and emissions associated with the material and fuel use of ship manufacturing. We used a bottom‐up emission model and global marine traffic data to estimate the operational footprint of different types of ships. Based on 382 cargo ships (including bulk carriers, container ships, and general cargo ships) constructed in mainland China between 2011 and 2015, we estimated that the embodied footprint accounted for <10% of the cradle‐to‐propeller footprint under the pre‐2019 policy scenario. In terms of life‐cycle energy intensity (MJ/nm/1000 deadweight tonnage [DWT]), the large bulk carrier (>100,000 DWT) establishes the lowest value (46), followed by the small (0–100,000 DWT) bulk carrier (96), the large container ship (133), the small container ship (196), and the small general cargo (238). The bulk carrier was identified as the most energy efficient among the three ship types, and ships with larger capacities (i.e., DWT) had higher energy efficiencies than ships with lower capacities. Our study provides a comprehensive understanding of the life‐cycle footprints of cargo ships, thus enabling better evidence‐based policymaking to transition the global marine‐shipping industry to a future of greener energy. [ABSTRACT FROM AUTHOR]

As a usual component in virtual scenes, water surface plays an important role in various graphical applications, including special effects, video games, and virtual reality. Although recent years have witnessed significant progress based on Navier–Stokes equations and simplified water models, large‐scale water surface waves with high‐frequency visual details remain computationally expensive for interactive applications. This article proposes a novel frequency‐aware neural network to synthesize consistent and detailed water surface waves from low‐resolution input. At its core, our approach leverage the wavelet transformation theory over space, frequency and direction, and incremental supervision to decompose the 4D amplitude function into multiple smaller subproblems. Specifically, we first customize four subnetworks and corresponding loss functions for super‐resolution of spatial resolution, temporal evolution, wave direction subdivision, and wave number, respectively. Then, to enforce the upsampling along each dimension orthogonal to each other, we introduce a cooperative training scheme to fine‐tune and integrate the proposed subnetworks with carefully designed training dataset. Our method can visually enhance high‐resolution spatial details, temporal coherence, interactions with complex boundaries, and various wave patterns with flexible control along multiple dimensions. Through extensive experiments, our method arrives at 13×$$ \times $$ speedup for 32×$$ \times $$ upsampling of various simulation scenarios. We also validate the effectiveness and robustness of our method to produce realistic water surface waves toward artistic innovation. [ABSTRACT FROM AUTHOR]

3D scene graph generation (SGG) aims to predict the class of objects and predicates simultaneously in one 3D point cloud scene with instance segmentation. Since the underlying semantic of 3D point clouds is spatial information, recent ideas of the 3D SGG task usually face difficulties in understanding global contextual semantic relationships and neglect the intrinsic 3D visual structures. To build the global scope of semantic relationships, we first propose two types of Semantic Clue (SC) from entity level and path level, respectively. SC can be extracted from the training set and modeled as the co‐occurrence probability between entities. Then a novel Semantic Clue aware Graph Convolution Network (SC‐GCN) is designed to explicitly model each SC of which the message is passed in their specific neighbor pattern. For constructing the interactions between the 3D visual and semantic modalities, a visual‐language transformer (VLT) module is proposed to jointly learn the correlation between 3D visual features and class label embeddings. Systematic experiments on the 3D semantic scene graph (3DSSG) dataset show that our full method achieves state‐of‐the‐art performance. [ABSTRACT FROM AUTHOR]

Despite the rapid development and proliferation of computer graphics hardware devices for scene capture in the most recent decade, the high‐resolution 3D/4D acquisition of gaseous scenes (e.g., smokes) in real time remains technically challenging in graphics research nowadays. In this paper, we explore a hybrid approach to simultaneously taking advantage of both the model‐centric method and the data‐driven method. Specifically, this paper develops a novel conditional generative model to rapidly reconstruct the temporal density and velocity fields of gaseous phenomena based on the sequence of two projection views. With the data‐driven method, we can achieve the strong coupling of density update and the estimation of flow motion, as a result, we can greatly improve the reconstruction performance for smoke scenes. First, we employ a conditional generative network to generate the initial density field from input projection views and estimate the flow motion based on the adjacent frames. Second, we utilize the differentiable advection layer and design a velocity estimation network with the long‐term mechanism to help achieve the end‐to‐end training and more stable graphics effects. Third, we can re‐simulate the input scene with flexible coupling effects based on the estimated velocity field subject to artists' guidance or user interaction. Moreover, our generative model could accommodate single projection view as input. In practice, more input projection views are enabling and facilitating the high‐fidelity reconstruction with more realistic and finer details. We have conducted extensive experiments to confirm the effectiveness, efficiency, and robustness of our new method compared with the previous state‐of‐the‐art techniques. [ABSTRACT FROM AUTHOR]

In this article, we propose a novel hybrid framework by combining smoothed particle hydrodynamics and adaptive narrow band fluid implicit particle method (NB‐FLIP) to faithfully model the multiphysical processes involving heat transfer and phase transition, and to precisely simulate the dynamics of condensed droplets moving along intricate objects. We first formulate a governing physical model built upon an improved phase transition model and an augmented on‐surface drop analysis method to achieve realistic condensation effects over intricate hydrophilic/hydrophobic interface. To achieve both high‐fidelity interactions and high‐resolution visual effects, we further develop an adaptive NB‐FLIP solver with octree‐dictated background grid in order to further enhance the performance of our framework. Experimental results have shown that our approach can be used to efficiently and realistically simulate the small‐scale interaction details between condensed drops and complex objects with arbitrary geometry. [ABSTRACT FROM AUTHOR]

In this paper, we articulate a novel plastic phase‐field (PPF) method that can tightly couple the phase‐field with plastic treatment to efficiently simulate ductile fracture with GPU optimization. At the theoretical level of physically‐based modeling and simulation, our PPF approach assumes the fracture sensitivity of the material increases with the plastic strain accumulation. As a result, we first develop a hardening‐related fracture toughness function towards phase‐field evolution. Second, we follow the associative flow rule and adopt a novel degraded von Mises yield criterion. In this way, we establish the tight coupling of the phase‐field and plastic treatment, with which our PPF method can present distinct elastoplasticity, necking, and fracture characteristics during ductile fracture simulation. At the numerical level towards GPU optimization, we further devise an advanced parallel framework, which takes the full advantages of hierarchical architecture. Our strategy dramatically enhances the computational efficiency of preprocessing and phase‐field evolution for our PPF with the material point method (MPM). Based on our extensive experiments on a variety of benchmarks, our novel method's performance gain can reach 1.56× speedup of the primary GPU MPM. Finally, our comprehensive simulation results have confirmed that this new PPF method can efficiently and realistically simulate complex ductile fracture phenomena in 3D interactive graphics and animation. [ABSTRACT FROM AUTHOR]

We present a deep learning based technique that enables novel‐view videos of human performances to be synthesized from sparse multi‐view captures. While performance capturing from a sparse set of videos has received significant attention, there has been relatively less progress which is about non‐rigid objects (e.g., human bodies). The rich articulation modes of human body make it rather challenging to synthesize and interpolate the model well. To address this problem, we propose a novel deep learning based framework that directly predicts novel‐view videos of human performances without explicit 3D reconstruction. Our method is a composition of two steps: novel‐view prediction and detail enhancement. We first learn a novel deep generative query network for view prediction. We synthesize novel‐view performances from a sparse set of just five or less camera videos. Then, we use a new generative adversarial network to enhance fine‐scale details of the first step results. This opens up the possibility of high‐quality low‐cost video‐based performance synthesis, which is gaining popularity for VA and AR applications. We demonstrate a variety of promising results, where our method is able to synthesis more robust and accurate performances than existing state‐of‐the‐art approaches when only sparse views are available. [ABSTRACT FROM AUTHOR]

Image color editing techniques are of great significance for users who wish to adjust the image color. However, previous works paid less attention to the translucent images. In this paper, we propose a new method to recolor the translucent images while preserving detailed information and color relationships of the source image. We consider the recolor problem as a location transformation problem and solve it in two steps: automatic palette extraction and homography estimation. First, we propose the Hmeans method to extract the dominant colors of the source image based on histogram statistics and clustering. Then, we propose homography estimation to map the source colors to desired colors in the CIE‐LAB color space. Further, we adopt a non‐linear optimization approach to improve the result generated by the last step. The proposed method maintains high fidelity of the source image. Experiments have shown that our method generates a state‐of‐the‐art visual result, in particular in the shadow areas. The source images with ground truth generated by a ray tracer further verify the effectiveness of our method. [ABSTRACT FROM AUTHOR]

Voxelization is a crucial process for many computer graphics applications such as collision detection, rendering of translucent objects, and global illumination. However, in some situations, although the mesh looks good, the voxelization resultmay be undesirable. In this paper,we describe a novel voxelizationmethod that uses the graphics processing unit for surface voxelization. Our improvements on the voxelization algorithm can address a problem of state-of-the-art voxelization, which cannot deal with thin parts of the mesh object.We improve the quality of voxelization on both normal mediation and surface correction. Furthermore, we investigate our voxelizationmethods on indirect illumination, showing the improvement on the quality of real-time rendering. [ABSTRACT FROM AUTHOR]

Many complex natural phenomena with dramatic spatial and temporal variation are difficult to animate accurately with anticipated performance in many graphics tasks and applications, because oftentimes in prior art, a single type of physical process could not afford high fidelity and effective scene production. Volcano eruption and its subsequent interaction with earth is one such complicated phenomenon that must depend on multiphysical processes and their tight coupling. This paper documents a novel and effective particle-based solution for volcano animation that embraces multiphysical processes and their tight unification. First, we introduce a governing physical model consisting of multiphysical processes enabling flexible state transition among solid, fluid, and gas. This computational physics model is dictated by temperature and accommodates dynamic viscosity that is changing according to the temperature. Second, we propose an augmented smoothed particle hydrodynamics as the underlying numerical model to simulate the behavior of lava and smoke with several required physical attributes. Third, multiphysical quantities are tightly coupled to support the interaction with surroundings including fluid-solid coupling, ground friction, and lava-smoke coupling. We also develop a temperature-directed rendering technique with nearly no extra computational cost and demonstrate realistic graphics effects of volcano eruption and its interaction with earth with visual appeal. [ABSTRACT FROM AUTHOR]

Animating debris flow is one of the most challenging tasks in computer graphics, because of its complex dynamic mechanism and the interaction between flows and solids in so large scale region. The difficulty focuses on how to resolve the contradiction between lower computational load and higher request of animating quality. A highly effective method of modeling and animating of debris flow with adaptive grid is presented. First, the debris flow is modeled as Bingham plastic fluid with view-dependent adaptive grid that is adopted to model the flow volume, and the boundless grids can cover the large scale region of debris flow. Then the mixed grids are built for confluent flows, and the two-way coupling interaction between flows and environment is considered. After extracting the debris flow surface, adaptive surface tension combining wave particles equation is used to enhance the details and sprays are generated by particles considering the interaction between two fluid volumes. Finally, different dynamic realistic scenes with debris flow are successfully animating at interactive rates. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents a hybrid animation approach to the flexible and rapid crack simulation of brittle material. At the physical level, the local stress tensors induced by collision are analyzed by using the smoothed particle hydrodynamics (SPH) formulation. Specifically, in order to determine the internal stress when rigid bodies collide with each other or neighboring environments, we treat all of them as completely rigid body that has infinite stiffness and then evaluate virtual displacement for colliding particles. At the geometric level, in order to faithfully maintain the fracture interface during the crack simulation, we utilize an efficient shape representation of solid based on the tetrahedral decomposition of the original solid geometry. This novel hybrid approach resorts to local particle models, whose goal is to avoid heavy computational burden during crack interface updating and topological changing, and meanwhile, it facilitates the user-initiated interactive control during the crack generation and propagation. Our animation experiments demonstrate the effectiveness of our novel particle-based method to simulate the crack of brittle material. Copyright © 2013 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT Physically based multiphase fluid simulation has been a hot topic in computer graphics. Since there are complex changing interface topology and interactions among air, solid, and different fluids, few papers have devoted to simulate the multiple fluids phenomena with solid-liquid phase transition. In this paper, the thermal fluid model for phase transition combined with free surface tracking is used to describe the interaction between air and fluids. Then a new model based on hierarchical lattice is proposed to process the solid-liquid interaction and the phase transition in the solid-liquid interface. Further, with the use of hybrid interaction with multidistribution functions, different realistic multiple fluids phenomena are rendered with different lattice sizes. Copyright © 2012 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

ABSTRACT The visual world around us displays a rich set of light effects because of translucent and participating media. It is hard and time consuming to render these effects with scattering, caustic, and shaft because of the complex interaction between light and different media. This paper presents a new rendering method based on adaptive lattice for lighting participating media of translucent materials such as marble, wax, and shaft light. Firstly, on the basis of the lattice-based photon tracing model, multi-scale hierarchical lattice was constructed by mixed lattice types sampling combined cubic Cartesian and face-centered cubic with view-dependent adaptive resolution. Then, an adaptive method to trace diffuse photons and marked specular photons with different phase functions was suggested. Multiple lights and heterogeneous materials were also considered here. Further, the mixed rendering method and GPU accelerate technology were introduced to render different light effects under different participating media. Copyright © 2011 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Simulation of dynamic natural scene is one of the most challenging tasks in computer graphics. In this paper, we propose a new approach to dynamic modeling and rendering of grasses wagging in wind. Through length preserving free-form deformation of the 3D skeleton lines of each grass blade and using the alpha test to implement transparent texture mapping, we successfully model grasses of different shapes with rich details. To simulate the real time waggle of grasses, the grasses of a meadow are represented in LOD, while their skeleton lines are dynamically deformed according to some physical models. Simplification technique is also employed to accelerate the collision detection between neighboring grasses. Experiments show that our method can realistically render the animated grass scenes under wind of different speeds and types in real time. Copyright © 2005 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Generating realistic spray details in liquid simulations remains computationally expensive. This paper proposes a data‐driven method to simulate high‐resolution sprays on low‐resolution grids by retrieving details with the most compatible details from a precomputed repository efficiently. We first employ a random forest‐based distance (RFD) to measure the similarity of liquid regions. In consideration of spatiotemporal relationships between one liquid region and its neighbors, we define a multinary label for RFD instead of the original binary one. Our improved RFD enables us to retrieve details that fit ground truth the best. To ensure temporal continuity of our result and to generate new details from existing ones, we formulate a series of forests with a training set from different time steps. Then, we synthesize results of each forest according to their distances. Finally, we put the synthesis result in correct positions to generate desired sprays motion. In our method, a state‐of‐the‐art cascade forest is employed for a higher accuracy. Several experiments with various grid resolutions validate our method both in visual effect and computational cost. [ABSTRACT FROM AUTHOR]