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Recent Submissions

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Periodic Anderson Acceleration for GPU-based Elastic Body Simulation

(Association for Computing Machinery, 2026) Yang, Youyi; Navkar, Nikhil; Deng, Zhigang; Peters, Christoph; Kettunen, Markus; Stephen, Spencer

Vertex Block Descent (VBD) solves implicit time integration through parallel per-vertex Newton steps, offering unconditional stability and GPU efficiency for elastic body simulation. Because its block Gauss-Seidel update propagates information only locally, low-frequency error modes spanning the mesh decay slowly across iterations. Chebyshev acceleration improves asymptotic convergence, but does not directly eliminate the globally correlated residuals. We introduce periodic Anderson Acceleration (PAA) for VBD, where a small leastsquares mixing step is applied every K iterations between Chebyshev-accelerated sweeps. The two accelerators are complementary: Chebyshev drives fast local convergence while Anderson Acceleration (AA) corrects global error modes through cross-vertex coupling extracted from recent iterates. A fused GPU kernel computes the full Gram system in a single pass, limiting overhead to under 5%. We show that periodic application with a window of m=2 outperforms every-iteration AA, as Chebyshev-accelerated iterates sampled at intervals provide a more linearly independent mixing basis. Combined with per-tet stiffness ramping, PAA reduces the final elastic energy by 6-49% over Chebyshev-only VBD at less than 5% wall-clock overhead, with the largest gains on geometrically complex meshes and extreme deformations. PAA requires no global matrix assembly, no mesh hierarchy, and adds a single integer parameter.

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Real-Time Dissectible Deformable Models Using High-Performance Breakable Shape Constraints

(Association for Computing Machinery, 2026) Mcgraw, Tim; Myers, Jack; Peters, Christoph; Kettunen, Markus; Stephen, Spencer

In this work we describe an approach to creating virtual dissectible models from color cryosection data, such as the images from the Visible Human Project, using high-performance deformable and destructible shape constraints. Prior research on deformable body fracture has largely focused on achieving physically accurate simulations, usually at the expense of real-time performance. We sacrifice engineering-level simulation accuracy, as would be expected in a surgical simulation, in favor of visually plausible animation. We use position based dynamics (PBD) to implement the dissectible model as a grid of constrained particles displayed as textured volumetric splats. Results are shown for a variety of real-world datasets, and we demonstrate fracture modes that are crucial to anatomy education applications: cutting, tearing and excision. A performance analysis shows that our compute shader implementation is capable of faster real-time performance than MPM and FEM, making it suitable for this application.

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Environmental Volumetric Neural Shading of Clouds for Real-Time Rendering

(Association for Computing Machinery, 2026) Olajos, Rikard; Doggett, Michael; Goswami, Prashant; Peters, Christoph; Kettunen, Markus; Stephen, Spencer

We present a high-performance method for real-time relighting of high-fidelity volumetric clouds. Building on Relightable Neural Assets, we introduce key adaptations that enable neural shading of volumetric cloud phenomena within a rasterization-based pipeline. In particular, we incorporate a per-pixel thickness parameter to capture view-dependent opacity and replace the generalizing single light source with a sky illumination model, allowing the network to learn complex atmospheric scattering effects. To achieve real-time performance, we depart from density-field-based volumetric rendering and instead operate on mesh representations combined with a triplane feature encoding. This enables a fully rasterization-driven solution that reproduces volumetric appearance without requiring ray marching or volume integration. We further describe a complete pipeline for converting volumetric cloud assets into a neural representation trained from path-traced supervision. We evaluate our method through an ablation study analyzing both image quality and runtime performance. Our adaptations improve reconstruction quality from 18.13 dB to 22.32 dB while achieving rendering times as low as 3.7 ms per frame. These results demonstrate that our approach enables high-quality, relightable cloud rendering suitable for real-time and performance-critical applications.

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High-Performance Real-Time Implicit Strand-Based Hair Rendering via Software Rasterization

(Association for Computing Machinery, 2026) Lipp, Lukas; Jarabo, Adrian; Wimmer, Michael; Bode, Lukas; Peters, Christoph; Kettunen, Markus; Stephen, Spencer

In this work we propose an efficient deferred software rasterization pipeline for real-time rendering of strandbased hair using hair meshes. Hair plays a crucial role in creating expressive 3D characters, yet strand-based approaches are often restricted to high-end hardware and typically applied to only a small number of hero characters. Hair meshes have proven to be an efficient representation capable of handling a wide variety of groom styles, but existing mesh shader-based implementations still suffer from significant bottlenecks. In this work, we address these limitations with a software rasterization approach that improves performance and compatibility. Our method enables efficient far-field strand-based hair rendering—even at a single sample per pixel—by combining deferred shading with a strand filtering and reconstruction step, while requiring only minimal hardware support. To further enhance scalability, we introduce a level-of-detail (LOD) scheme that adapts hair representation and shading complexity based on viewing distance and screen-space coverage, reducing computational cost further while preserving visual fidelity. To the best of our knowledge, this is the first approach to achieve this combination of efficiency, flexibility, scalability, and broad hardware compatibility.

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Warnock: Harnessing GPU Geometry Amplification for Vector Graphics

(Association for Computing Machinery, 2026) Kilgard, Mark J.; Barran, Brian; Peters, Christoph; Kettunen, Markus; Stephen, Spencer

Warnock is a novel 2D vector graphics library harnessing the programmable geometry amplification features of GPUs. Warnock refines the theory of polar stroking by providing a practical pixel-space quality metric; furthermore, it extends polar stroking to also fill paths. Warnock converts a path into a single compact GPU buffer so this one buffer has all the necessary data to both fill and stroke the path. Unlike prior art, this one GPU buffer handles all variations in transformation, stroke width, capping, and join styles. Warnock supports older DirectX 11-class GPUs with tessellation shaders, but recent GPUs with mesh shading perform best.