This is a corrigendum for the article “Wavelet Fluids” published in ACM Trans. Graph. 44, 6, Article 270 (December 2025), 17 pages.

Medical image super-resolution (SR) is essential for enhancing diagnostic accuracy while reducing acquisition cost and scanning time. However, modeling both long-range anatomical structures and fine-grained frequency details with low computational overhead remains challenging. We propose FGMamba, a novel frequency-aware gated state-space model that unifies global dependency modeling and fine-detail enhancement into a lightweight architecture. Our method introduces two key innovations: a Gated Attention-enhanced State-Space Module (GASM) that integrates efficient state-space modeling with dualbranch spatial and channel attention, and a Pyramid Frequency Fusion Module (PFFM) that captures high-frequency details across multiple resolutions via FFT-guided fusion. Extensive evaluations across five medical imaging modalities (Ultrasound, OCT, MRI, CT, and Endoscopic) demonstrate that FGMamba achieves superior PSNR/SSIM while maintaining a compact parameter footprint (<0.75M), outperforming CNN-based and Transformerbased SOTAs. Our results validate the effectiveness of frequencyaware state-space modeling for scalable and accurate medical image enhancement. Source code and dataset will be made publicly available.

Microsurgical clipping (MC) is widely used for the treatment of intracranial aneurysms (IA). However, it is a high risk for neurosurgeons to perform this operation due to the complex intracranial anatomy, limited microscopic view, and confined operational space. To tackle the above issues, meticulous preoperative rehearsal is in urgent need to improve the neurosurgeons’ perception of patient-specific anatomy while designing the optimal surgical plan, which can greatly enhance the patients’ safety. Most existing MC simulators only support limited interaction methods, such as haptic devices, leading to substantial differences between the training experience and actual surgical situations. To this end, we present a mixed reality (MR) interface for IA MC rehearsal, which can provide neurosurgeons with a more immersive experience. Firstly, considering the labor-intensive labeling cost of reconstructing a 3D full-brain vascular model from CTA images, the simulator employs a cost-effective specific-to-general vessel stitching technique to generate 3D lesion-specific IA geometric models, which replaces normal vascular segments in a standard brain with a personalized-specific operating region by a scaling-constrained iterative closest point (ICP) algorithm. Besides, we design a marker-based tracking method allowing accessible natural human-computer interaction using real surgical instruments which can fuse virtual anatomy and real operation environments, enhancing the users’ spatial perception and tangible stimuli. Additionally, to ensure simulation stability while providing visually plausible clipping operations, we develop a collision distance constrained position-based dynamics (PBD) method with low-resolution sampling particles to simulate the deformation of aneurysm vessels. Quantitative experiments demonstrate the accuracy of our surgical instruments tracking and vessel deformation simulation, which can also fulfill the real-time performance of MC rehearsal. User study indicates that virtual rehearsals significantly improve spatial awareness and dexterity in handling aneurysms, and have the great potential to be applied in practical applications.

This paper introduces a novel wavelet-based framework for simulating both single-phase (e.g., smoke) and two-phase (e.g., bubbly water) flows, featuring unified boundary condition handling for free surfaces and solid obstacles. In liquid simulations, conventional pressure projection methods enforce zero-pressure Dirichlet conditions at free surfaces by solving a simplified pressure Poisson equation. However, these approaches neglect air-phase incompressibility, leading to artificial bubble collapse. Stream function methods overcome this limitation by solving a density-variable vector potential Poisson equation, ensuring incompressibility in both simulated and unsimulated regions while maintaining divergence-free liquid phases independent of solver accuracy. Yet, they triple the linear system's dimensionality and exhibit poor convergence near solid boundaries. The fundamental limitation of both methods stems from their governing equations: singularities emerge as density approaches extreme values. The pressure Poisson equation becomes ill-conditioned when density nears zero (air phase), compromising air-phase incompressibility, while the vector potential equation degrades as density approaches infinity (solid phase), impeding solid-boundary convergence. To address these singularities, we first propose a novel decomposition where zero and infinite densities are well-defined. We then reformulate this decomposition as a fixed-point iteration using density-agnostic curl-free and divergence-free projections, eliminating the need for linear system solves. The error equation is derived, and a necessary and sufficient convergence condition is established. Building on this, we develop an iterative algorithm that efficiently solves the fixed-point problem through alternating wavelet-based non-orthogonal curl-free and divergence-free projections. Additionally, we investigate orthogonal curl-free projections (e.g., Fourier methods) and their complementary divergence-free counterparts, providing a comprehensive comparison between wavelet and Fourier approaches. Our method simultaneously computes pressure and stream functions, retaining the incompressibility benefits of stream function approaches while resolving their computational inefficiencies and solid-boundary convergence issues. Experiments demonstrate our framework's ability to efficiently simulate complex two-phase phenomena, such as the glugging effect during water pouring and multi-liquid-region interactions across zero-density air.

Abstract We introduce an efficient technique for recovering the vector potential in wavelet space to simulate pointwise incompressible fluids. This technique ensures that fluid velocities remain divergence‐free at any point within the fluid domain and preserves local volume during the simulation. Divergence‐free wavelets are utilized to calculate the wavelet coefficients of the vector potential, resulting in a smooth vector potential with enhanced accuracy, even when the input velocities exhibit some degree of divergence. This enhanced accuracy eliminates the need for additional computational time to achieve a specific accuracy threshold, as fewer iterations are required for the pressure Poisson solver. Additionally, in 3D, since the wavelet transform is taken in‐place, only the memory for storing the vector potential is required. These two features make the method remarkably efficient for recovering vector potential for fluid simulation. Furthermore, the method can handle various boundary conditions during the wavelet transform, making it adaptable for simulating fluids with Neumann and Dirichlet boundary conditions. Our approach is highly parallelizable and features a time complexity of O(n), allowing for seamless deployment on GPUs and yielding remarkable computational efficiency. Experiments demonstrate that, taking into account the time consumed by the pressure Poisson solver, the method achieves an approximate 2x speedup on GPUs compared to state‐of‐the‐art vector potential recovery techniques while maintaining a precision level of 10−6 when single float precision is employed. The source code of ‘Wavelet Potentials’ can be found in https://github.com/yours321dog/WaveletPotentials .

The interaction of gas and liquid can produce many interesting phenomena, such as bubbles rising from the bottom of the liquid. The simulation of two-phase fluids is a challenging topic in computer graphics. To animate the interaction of a gas and liquid, MultiFLIP samples the two types of particles, and a Euler grid is used to track the interface of the liquid and gas. However, MultiFLIP uses the fluid implicit particle (FLIP) method to interpolate the velocities of particles into the Euler grid, which suffer from additional noise and instability. To solve the problem caused by fluid implicit particles (FLIP), we present a novel velocity transport technique for two individual particles based on the affine particle-in-cell (APIC) method. First, we design a weighed coupling method for interpolating the velocities of liquid and gas particles to the Euler grid such that we can apply the APIC method to the simulation of a two-phase fluid. Second, we introduce a narrowband method to our system because MultiFLIP is a time-consuming approach owing to the large number of particles. Experiments show that our method is well integrated with the APIC method and provides a visually credible two-phase fluid animation. The proposed method can successfully handle the simulation of a twophase fluid.

Abstract Physically plausible fracture animation is a challenging topic in computer graphics. Most of the existing approaches focus on the fracture of isotropic materials. We proposed a frame‐field method for the design of anisotropic brittle fracture patterns. In this case, the material anisotropy is determined by two parts: anisotropic elastic deformation and anisotropic damage mechanics. For the elastic deformation, we reformulate the constitutive model of hyperelastic materials to achieve anisotropy by adding additional energy density functions in particular directions. For the damage evolution, we propose an improved phase‐field fracture method to simulate the anisotropy by designing a deformation‐aware second‐order structural tensor. These two parts can present elastic anisotropy and fractured anisotropy independently, or they can be well coupled together to exhibit rich crack effects. To ensure the flexibility of simulation, we further introduce a frame‐field concept to assist in setting local anisotropy, similar to the fiber orientation of textiles. For the discretization of the deformable object, we adopt a novel Material Point Method(MPM) according to its fracture‐friendly nature. We also give some design criteria for anisotropic models through comparative analysis. Experiments show that our anisotropic method is able to be well integrated with the MPM scheme for simulating the dynamic fracture behavior of anisotropic materials.

Abstract Physically based deformation simulation has been studied for many years in computer graphics. In order to simulate more complex geometric models and better meet the designer's requirements, many anisotropic approaches have been proposed in recent years. However, most of the approaches focus on simulating orthotropic models. In comparison with orthotropic models, nonorthotropic ones allow the objects to have anisotropic behaviors along nonorthogonal directions. In this paper, we introduce an improved approach to simulate nonorthotropic geometric models under large deformation. The improvements are mainly twofold. First, a frame field is specified on a given undeformed object, that is, each point of the object is equipped with a frame. In each local frame, we construct three independent vectors and form a nonorthogonal coordinate. Second, we design the deformation properties along each axis in the local nonorthogonal coordinate to get a local constitutive model. The final nonorthotropic model is generated by transforming the designed model from local nonorthogonal coordinates to the global standard Cartesian coordinate. To improve the stability, we introduce a time‐varying method to simultaneously track the local coordinates reorientation by pushing forward the original frame field to the deformed frame field. Experiments show that the deformation simulation using the designed nonorthotropic models exhibits anisotropic behaviors along different directions and are more stable than previous methods.

Abstract We introduce a new biorthogonal wavelet approach to creating a water‐tight surface defined by an implicit function, from a finite set of oriented points. Our approach aims at addressing problems with previous wavelet methods which are not resilient to missing or nonuniformly sampled data. To address the problems, our approach has two key elements. First, by applying a three‐dimensional partial integration, we derive a new integral formula to compute the wavelet coefficients without requiring the implicit function to be an indicator function. It can be shown that the previously used formula is a special case of our formula when the integrated function is an indicator function. Second, a simple yet general method is proposed to construct smooth wavelets with small support. With our method, a family of wavelets can be constructed with the same support size as previously used wavelets while having one more degree of continuity. Experiments show that our approach can robustly produce results comparable to those produced by the Fourier and Poisson methods, regardless of the input data being noisy, missing or nonuniform. Moreover, our approach does not need to compute global integrals or solve large linear systems.