🤖 AI Summary
Rendering complex woven fabrics—comprising fibers, plies, and yarns—with photorealism remains computationally expensive, as balancing geometric detail and rendering efficiency is challenging. To address this, we propose a multiscale yarn appearance model: (1) using yarns as the geometric core, we implicitly generate ply and fiber structures; (2) we design a four-component, direction-aware scattering BRDF supporting anisotropic light transport; and (3) we introduce an adaptive multiscale rendering framework based on pixel coverage, integrating geometric aggregation for dimensionality reduction and implicit shape modeling. Experiments demonstrate that our method accelerates near-field rendering by 3–5× while reducing GPU memory consumption; achieves ~20% speedup for distant fabric regions; and significantly enhances fiber-level geometric fidelity and lighting realism. Overall, our approach establishes a new, efficient, and scalable paradigm for high-fidelity cloth rendering.
📝 Abstract
Rendering realistic cloth has always been a challenge due to its intricate structure. Cloth is made up of fibers, plies, and yarns, and previous curved-based models, while detailed, were computationally expensive and inflexible for large cloth. To address this, we propose a simplified approach. We introduce a geometric aggregation technique that reduces ray-tracing computation by using fewer curves, focusing only on yarn curves. Our model generates ply and fiber shapes implicitly, compensating for the lack of explicit geometry with a novel shadowing component. We also present a shading model that simplifies light interactions among fibers by categorizing them into four components, accurately capturing specular and scattered light in both forward and backward directions. To render large cloth efficiently, we propose a multi-scale solution based on pixel coverage. Our yarn shading model outperforms previous methods, achieving rendering speeds 3-5 times faster with less memory in near-field views. Additionally, our multi-scale solution offers a 20% speed boost for distant cloth observation.