Unsupervised 2D image-based 3D model retrieval (IBMR) aims to retrieve semantically relevant 3D shapes for a given 2D image query when 3D annotations are unavailable. This setting is challenging due to severe modality gaps, category-imbalanced mini-batches, inconsistent cross-domain decision boundaries, and mismatched semantic neighborhood structures. In this paper, we propose a unified framework that integrates Category-Aligned Sampling (CAS), Decision Boundary Alignment (DBA), and Graph Semantic Propagation (GSP) into a single optimization paradigm. CAS constructs category-consistent mini-batches to stabilize crossmodal learning. Built upon CAS, DBA leverages a masked Margin Disparity Discrepancy to regularize cross-domain class decision boundaries via an adversarial min-max objective, encouraging discriminative separation beyond marginal feature matching. To complement boundary-level regularization, GSP builds a crossdomain affinity graph over 2D and 3D samples and propagates supervision-induced relational structure through semantic message passing, explicitly preserving instance-level neighborhood consistency that is critical for retrieval. Extensive experiments on MI3DOR and MI3DOR-2 demonstrate consistent improvements over representative unsupervised IBMR baselines.

Deepfake detection aims to identify manipulated content generated by generative models such as GANs and diffusion models. Although many detection methods have been proposed in recent years, their performance often degrades significantly when the test data includes unknown images or novel forgery types. To address this challenge, we propose a Realistic Sample Generation and Dual Augmentation framework, abbreviated as RSG-DA, to enhance generalization in deepfake detection. The key idea is to explore and expand the forgery feature space in order to learn decision boundaries that can capture diverse forgery patterns. Specifically, we introduce a Dynamic Landmark Diffusion Generator (DLDG) that synthesizes hybrid forgery samples with high visual realism and structural diversity. Additionally, we design a Dual Data Augmentation (DDA) strategy composed of DW-Augmentation and Class-Augmentation, where DW-Augmentation strengthens the representation of authentic image features through multi-scale transformations, while Class-Augmentation enriches the forgery distribution by expanding it with varied manipulations. Finally, we present a Lightweight Generic Forgery Distillation (LGFD) module that integrates the above components into a unified encoder, enabling the learning of robust and transferable forgery representations. Extensive experiments show that our method consistently outperforms state-of-the-art approaches in both intra-dataset and cross-dataset evaluations.

Temporal Action Detection aims to localize and classify action instances within untrimmed videos, yet it remains challenging due to background clutter, high intra-class similarity, and varied temporal scales in real-world scenarios. To address these issues, we propose the Local-Pattern Separation and Global-Aware Network (LSGNet) tailored for temporal action localization. Specifically, the core of LSGNet is the Local Pattern Separation Module (LPSM), which explicitly models both consistency and variation patterns of action segments within local temporal windows. Additionally, to capture comprehensive contextual information, we introduce the Global Context-Aware Representation Module (GCRM), which decouples temporal features across multiple granularities and enables robust modeling of long-range dependencies. Finally, we design the Multi-scale Feature Refinement Module (MFRM) to mitigate the degradation of fine-grained information by performing iterative reconstruction across temporal scales, thereby enriching semantic representations and preserving temporal details. Extensive experiments on THUMOS14, ActivityNet1.3, HACS, and EPIC-Kitchens-100 demonstrate the effectiveness of the proposed LSGNet method. Additional ablation studies on the QVHighlights dataset further confirm the generalization capability of LPSM module in video moment retrieval and highlight detection, achieving consistent improvements in retrieval accuracy and localization precision.

Despite the material point method (MPM) provides a unified particle simulation framework for coupling of different materials, MPM suffers from sticky numerical artifacts, which is inherently restricted to sticky and no-slip interactions. In this paper, we propose a novel transfer scheme called Decomposed Compatible Affine Particle in Cell (DC-APIC) within the MPM framework for simulating the two-way coupled interaction between elastic solids and incompressible fluids under free-slip boundary conditions on a unified background grid. Firstly, we adopt particle-grid compatibility to describe the relationship between grid nodes and particles at the fluid–solid interface, which serves as the guideline for subsequent particle–grid–particle transfers. Then we develop a phase-field gradient method to track the compatibility and normal directions at the interface. Secondly, to facilitate automatic MPM collision resolution during solid–fluid coupling, in the proposed DC-APIC integrator, the tangential component will not be transferred between incompatible grid nodes to prevent velocity smoothing in another phase, while the normal component is transferred without limitations. Finally, our comprehensive results confirm that our approach effectively reduces diffusion and unphysical viscosity compared to traditional MPM. • Developed a decomposed compatible APIC transfer scheme to reduce numerical viscosity on a unified grid. • Modified traditional MPM with DC-APIC integrator to enforce free-slip and separation boundary conditions. • Created an efficient parallel framework utilizing hierarchical GPU architecture for phase-field gradient method.

Digital elevation models (DEMs) are the fundamental for modeling and analyzing spatial topographic information in geographic information system, 3D video games, and many other fields. However, due to various terrain factors in data acquisition, open access datasets often contain inaccurate data or miss data, leading to undesirable models. This paper proposes a terrain refinement method based on shadow constraints by taking full advantages of differentiable rendering enabled efficient optimization. To be specific, we introduce an iterative approach to optimize shadow masks from satellite images based on differentiable rendering, which provides extra geometric clues for further terrain refinement. Thereafter, we propose to synthesize high-quality data in a randomization manner via differentiable renderer to expose the latent correlation between shadow distribution and terrain geometry, and generalize to real-world DEMs. Moreover, structure lines extracted from forward rendering results are also utilized to provide comprehensive geometric constraints for terrains. Extensive experiments demonstrate the effectiveness of our proposed methods.

Abstract Evacuation scene simulation has become one important approach for public safety decision‐making. Although existing research has considered various factors, including social forces, panic emotions, and so forth, there is a lack of consideration of how complex environmental factors affect human psychology and behavior. The main idea of this paper is to model complex evacuation environmental factors from the perspective of knowledge and explore pedestrians' emergency response mechanisms to this knowledge. Thus, a knowledge‐driven dynamic perception model (KDPM) for evacuation scene simulation is proposed in this paper. This model combines three modules: knowledge dissemination, dynamic scene perception, and stress response. Both scenario knowledge and hazard source knowledge are extracted and expressed. The improved intelligent agent perception model is designed by adopting position determination. Moreover, a general adaptation syndrome (GAS) model is first presented by introducing a modified stress system model. Experimental results show that the proposed model is closer to the reality of real data sets.

Abstract 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.

Recent years have witnessed the rapid deployment of numerous physics-based modeling and simulation algorithms and techniques for fluids, solids, and their delicate coupling in computer animation. However, it still remains a challenging problem to model the complex elastic-viscoplastic behaviors during fluid–solid phase transitions and facilitate their seamless interactions inside the same framework. In this article, we propose a practical method capable of simulating granular flows, viscoplastic liquids, elastic-plastic solids, rigid bodies, and interacting with each other, to support novel phenomena all heavily involving realistic phase transitions, including dissolution, melting, cooling, expansion, shrinking, and so on. At the physics level, we propose to combine and morph von Mises with Drucker–Prager and Cam–Clay yield models to establish a unified phase-field-driven EVP model, capable of describing the behaviors of granular, elastic, plastic, viscous materials, liquid, non-Newtonian fluids, and their smooth evolution. At the numerical level, we derive the discretization form of Cahn–Hilliard and Allen–Cahn equations with the material point method to effectively track the phase-field evolution, so as to avoid explicit handling of the boundary conditions at the interface. At the application level, we design a novel heuristic strategy to control specialized behaviors via user-defined schemes, including chemical potential, density curve, and so on. We exhibit a set of numerous experimental results consisting of challenging scenarios to validate the effectiveness and versatility of the new unified approach. This flexible and highly stable framework, founded upon the unified treatment and seamless coupling among various phases, and effective numerical discretization, has its unique advantage in animation creation toward novel phenomena heavily involving phase transitions with artistic creativity and guidance.

Abstract 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 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.

Constructing large-scale realistic terrains using modern modeling tools is an extremely challenging task even for professional users, undermining the effectiveness of video games, virtual reality, and other applications. In this paper, we present a step towards unsupervised and realistic modeling of textured terrains from DEM and satellite imagery, built upon two-stage illumination and texture optimization via differentiable rendering. First, a differentiable renderer for satellite imagery is established based on the Lambert diffuse model that allows inverse optimization of material and lighting parameters towards specific objective. Second, the original illumination direction of satellite imagery is recovered by reducing the difference between the shadow distribution generated by the renderer and that of the satellite image in YCrCb colour space, leveraging the abundant geometric information of DEM. Third, we propose to generate the original texture of the shadowed region by introducing visual consistency and smoothness constraints via differentiable rendering to arrive at an end-to-end unsupervised architecture. Comprehensive experiments demonstrate the effectiveness and efficiency of our proposed method as a potential tool to achieve virtual terrain modeling for widespread graphics applications.

Abstract 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 speedup for 32 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 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.

This article articulates a novel learning framework for both parameter estimation and detail enhancement for Eulerian gas based on data guidance. The key motivation of this article is to devise a new hybrid, grid-based simulation that could inherit modeling and simulation advantages from both physically-correct simulation methods and powerful data-driven methods, while combating existing difficulties exhibited in both approaches. We first employ a convolutional neural network (CNN) to estimate the physical parameters of gaseous phenomena in Eulerian settings, then we can use the just-learnt parameters to re-simulate (with or without artists' guidance) for specific scenes with flexible coupling effects. Next, a second CNN is adopted to reconstruct the high-resolution velocity field to guide a fast re-simulation on the finer grid, achieving richer and more realistic details with little extra computational expense. From the perspective of physics-based simulation, our trained networks respect temporal coherence and physical constraints. From the perspective of the data-driven machine-learning approaches, our network design aims at extracting a meaningful parameters and reconstructing visually realistic details. Additionally, our implementation based on parallel acceleration could significantly enhance the computational performance of every involved module. Our comprehensive experiments confirm the controllability, effectiveness, and accuracy of our novel approach when producing various gaseous scenes with rich details for widespread graphics applications.

Abstract 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 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 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 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.