Predicting Fabric Appearance Through Thread Scattering and Inversion

Abstract

The fashion industry has a real need to preview fabric designs using the actual threads they intend to use, ensuring that the designs they envisage can be physically realized. Unfortunately, today's fabric rendering relies on either hand-tuned parameters or parameters acquired from already fabricated cloth. Furthermore, existing curve-based scattering models are not suitable for this problem: they are either not naturally differentiable due to discrete fiber count parameters, or require a more detailed geometry representation, introducing extra complexity. In this work, we bridge this gap by presenting a novel pipeline that captures and digitizes physical threads and predicts the appearance of the fabric based on the weaving pattern. We develop a practical thread scattering model based on simulations of multiple fiber scattering within a thread. Using a cost-efficient multi-view setup, we capture threads of diverse colors and materials. We apply differentiable rendering to digitize threads, demonstrating that our model significantly improves the reconstruction accuracy compared to existing models, matching both reflection and transmission. We leverage a two-scale rendering technique to efficiently render woven cloth. We validate that our digital threads, combined with simulated woven yarn geometry, can accurately predict the fabric appearance by comparing to real samples. We show how our work can aid designs using diverse thread profiles, woven patterns, and textured design patterns.