Abstract Knitted fabrics present unique challenges for realistic rendering due to their complicated structure and scale‐dependent appearance. Existing methods typically rely on explicit yarn geometry, which is computationally complex, or texture‐based representations that require heavy storage and precomputed maps. In this paper, we introduce the first texture‐free, surface‐based appearance model for knitted fabrics, in which stitches are represented parametrically as thick curves and mapped directly onto fabric meshes. This avoids explicit yarn or fiber geometry, yet preserves the characteristic 3D look of yarn‐based models. Unlike prior surface‐based approaches, our method produces realistic volumetric effects such as depth, parallax, and silhouette preservation. To achieve this, we propose a curvature‐aware parallax mapping technique that ensures coherent appearance at grazing angles. Furthermore, we extend the appearance model to a multi‐scale formulation that aggregates geometry and visibility over texture footprints and adjusts roughness parameters for stable far‐field rendering. Our model combines the efficiency and simplicity of surface‐based methods with the volumetric realism of fiber‐based models, reproducing characteristic knit effects such as 3D stitch structure in a multi‐scale manner without the complexity or storage cost of texture‐based approaches.

We develop an algorithm for the efficient and stable simulation of large-scale elastic rod assemblies. We observe that the time-integration step is severely restricted by a strong nonlinearity in the response of stretching modes to transversal impact, the degree of this nonlinearity varying greatly with the shape of the rod. Building on these observations, we propose a collision response algorithm that adapts its degree of nonlinearity. We illustrate the advantages of the resulting algorithm by analyzing simulations involving elastic rod assemblies of varying density and scale, with up to 1.7 million individual contacts per time step.

Abstract We present a reflectance model for dielectric cylinders with rough surfaces such as human hair fibers. Our model is energy conserving and can evaluate arbitrarily many orders of internal reflection. Accounting for compression and contraction of specular cones produces a new longitudinal scattering function which is non‐Gaussian and includes an off‐specular peak. Accounting for roughness in the azimuthal direction leads to an integral across the hair fiber which is efficiently evaluated using a Gaussian quadrature. Solving cubic equations is avoided, caustics are included in the model in a consistent fashion, and more accurate colors are predicted by considering many internal pathways.