Mechanics-Aware Modeling of Cloth Appearance

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 mechanically plausible way.