arXiv subjects

Ryan Goldade

Publications and source records attributed to Ryan Goldade.

5 recordsLinked to original sources

arXiv2026

Physics-Based Simulation of Contact-Induced Facial Wrinkling

Facial skin dynamics are inherently challenging to simulate due to a combination of geometric, material, and anatomical complexities. Human skin is a nonlinear layered material with spatially heterogeneous attachments to the underlying tissues. During contact events, localized compression and shear induce mechanical instabilities, leading to fine-scale wrinkling patterns governed by a delicate interplay of geometry, boundary conditions, and through-the-thickness stresses. We present a finite element framework to simulate contact-induced wrinkling of facial skin. We model skin as a viscoelastic material with time-dependent relaxation that governs the rate, persistence, and damping of wrinkle formation. We employ high-order prismatic solid-shell elements to resolve through-thickness stresses and high-frequency deformation modes. Central to our approach, we introduce a continuum-based formulation of skin ligaments to model heterogeneous skin attachments and provide anatomically inspired mobility constraints. These skin ligaments control the formation and appearance of facial wrinkles by modulating their amplitude, wavelength, and spatial distribution. We evaluate our method on a set of synthetic examples and compare simulations with real-world footage. These results demonstrate that our skin model produces temporally coherent and visually realistic wrinkle patterns during transient contact.

cs.GR↗

arXiv2026

HyperBones: Realtime Bone-driven Neural Garment Simulation with Hypernetwork Conditioning

Recent advances in cloth simulation have led to accurate garment physics, but the methods are computationally expensive for real-time applications. In contrast, Linear Blend Skinning (LBS) is efficient, but cannot capture the complex dynamics of loose-fitting garments, leading to unrealistic motion and visual artifacts. Neural methods offer a promising alternative, yet they still struggle to animate loose clothing plausibly under strict runtime constraints. We present a fast and physically-informed framework for dynamic garment simulation, consisting of a reduced-space neural dynamics simulator with independent coarse and fine-level components. At the coarse level, the garment is driven by virtual bones integrated with a lightweight neural network for predicting corrections over LBS. Fine-scale wrinkle details are then recovered using a convolutional MLP defined in UV space. By decoupling identity-specific computation from shape conditioning via hypernetwork, our neural framework offers high performance, trained using an effective physics-based self-supervised training paradigm without relying on an offline simulator. Experiments show that our method produces physically plausible garment dynamics, generalizes across diverse motions and unseen body shapes, and delivers over 30x speedup compared to state-of-the-art autoregressive neural simulators, achieving interactive inference at ~1 ms per frame on a consumer GPU.

cs.GR↗

arXiv2026

PhySkin: Physics-based Bone-driven Neural Garment Simulation

Recent advances in digital avatar technology have enabled the generation of compelling virtual characters, but deploying these avatars on compute-constrained devices poses significant challenges for achieving realistic garment deformations. While physics-based simulations yield accurate results, they are computationally prohibitive for real-time applications. Conversely, linear blend skinning offers efficiency but fails to capture the complex dynamics of loose-fitting garments, resulting in unrealistic motion and visual artifacts. Neural methods have shown promise, yet they struggle to animate loose clothing plausibly under strict performance constraints. In this work, we present a novel approach for fast and physically plausible garment draping tailored for resource-constrained environments. Our method leverages a reduced-space quasi-static neural simulation, mapping the garment's full degrees of freedom to a set of bone handles that drive deformation. A neural deformation model is trained in a fully self-supervised manner, eliminating the need for costly simulation data. At runtime, a lightweight neural network modulates the handle deformations based on body shape and pose, enabling realistic garment behavior that respects physical properties such as gravity, fabric stretching, bending, and collision avoidance. Experimental results demonstrate that our method achieves physically plausible garment drapes while generalizing across diverse poses and body shapes, supporting zero-shot evaluation and mesh topology independence. Our method's runtime significantly outperforms past works, as it runs in microseconds per frame using single-threaded CPU inference, offering a practical solution for real-time avatar animation on low-compute devices.

cs.GR↗

arXiv2025

SkinCells: Sparse Skinning using Voronoi Cells

For decades, real-time skinning has been the cornerstone of character animation in visual effects and games. Despite its importance, the creation of animatable digital assets remains a labor-intensive manual process. Existing automated tools frequently struggle with intricate geometries, often necessitating significant manual refinement to reach production standards. We present a robust, fully automated method for generating high-quality skinning weights from a standard mesh and skeleton in a canonical A- or T-pose. Unlike traditional approaches, our framework offers direct sparsity controls to limit bone influences per vertex -- a critical requirement for maintaining performance in large-scale mobile environments. Furthermore, we address the challenge of Level-of-Detail (LoD) management by optimizing weights within a continuous spatial volume rather than on discrete vertices. This allows a single optimization pass to be applied seamlessly across multiple asset resolutions and variations. Central to our approach is a novel parameterized family of functions, we call SkinCells. We demonstrate that our method consistently produces stable, high-quality results even in complex scenarios where standard biharmonic weight computations fail.

cs.GR↗

arXiv2017

Constraint Bubbles: Adding Efficient Zero-Density Bubbles to Incompressible Free Surface Flow

Liquid simulations for computer animation often avoid simulating the air phase to reduce computational costs and ensure good conditioning of the linear systems required to enforce incompressibility. However, this free surface assumption leads to an inability to realistically treat bubbles: submerged gaps in the liquid are interpreted as empty voids that immediately collapse. To address this shortcoming, we present an efficient, practical, and conceptually simple approach to augment free surface flows with negligible density bubbles. Our method adds a new constraint to each disconnected air region that guarantees zero net flux across its entire surface, and requires neither simulating both phases nor reformulating into stream function variables. Implementation of the method requires only minor modifications to the pressure solve of a standard grid-based fluid solver, and yields linear systems that remain sparse and symmetric positive definite. In our evaluations, solving the modified pressure projection system took no more than 10% longer than the corresponding free surface solve. We demonstrate the method's effectiveness and flexibility by incorporating it into commercial fluid animation software and using it to generate a variety of dynamic bubble scenarios showcasing glugging effects, viscous and inviscid bubbles, interactions with irregularly-shaped and moving solid boundaries, and surface tension effects.

cs.GR↗