# Mechanics-Aware Modeling of Cloth Appearance

## TL;DR

This paper presents a novel physics-based model that captures how fabric microstructures change under mechanical forces, significantly improving the realism of cloth appearance rendering.

## Contribution

It introduces a mechanics-aware cloth appearance model that integrates yarn and fiber-level simulations with a new parameter fitting algorithm and neural network for realistic, dynamic rendering.

## Key findings

- The model produces photorealistic, mechanically plausible cloth appearances.
- It effectively captures microstructure changes due to external forces.
- The approach enables dynamic cloth rendering with high fidelity.

## Abstract

Micro-appearance models have brought unprecedented fidelity and details to cloth rendering. Yet, these models neglect fabric mechanics: when a piece of cloth interacts with the environment, its yarn and fiber arrangement usually changes in response to external contact and tension forces. Since subtle changes of a fabric's microstructures can greatly affect its macroscopic appearance, mechanics-driven appearance variation of fabrics has been a phenomenon that remains to be captured. We introduce a mechanics-aware model that adapts the microstructures of cloth yarns in a physics-based manner. Our technique works on two distinct physical scales: using physics-based simulations of individual yarns, we capture the rearrangement of yarn-level structures in response to external forces. These yarn structures are further enriched to obtain appearance-driving fiber-level details. The cross-scale enrichment is made practical through a new parameter fitting algorithm for simulation, an augmented procedural yarn model coupled with a custom-design regression neural network. We train the network using a dataset generated by joint simulations at both the yarn and the fiber levels. Through several examples, we demonstrate that our model is capable of synthesizing photorealistic cloth appearance in a %dynamic and mechanically plausible way.