Inverse Garment and Pattern Modeling with a Differentiable Simulator

TL;DR

This paper introduces a differentiable cloth simulation framework that recovers 2D garment patterns and material parameters from 3D shapes, enabling realistic virtual garment reproduction and manufacturing-ready patterns.

Contribution

It presents a novel inverse garment modeling method using a differentiable simulator to estimate patterns and material properties from 3D shapes, aligning with industry standards.

Findings

Successfully reproduces garment shapes and patterns from 3D data.

Produces manufacturing-ready, symmetric garment patterns.

Demonstrates effectiveness across various garment types.

Abstract

The capability to generate simulation-ready garment models from 3D shapes of clothed humans will significantly enhance the interpretability of captured geometry of real garments, as well as their faithful reproduction in the virtual world. This will have notable impact on fields like shape capture in social VR, and virtual try-on in the fashion industry. To align with the garment modeling process standardized by the fashion industry as well as cloth simulation softwares, it is required to recover 2D patterns. This involves an inverse garment design problem, which is the focus of our work here: Starting with an arbitrary target garment geometry, our system estimates an animatable garment model by automatically adjusting its corresponding 2D template pattern, along with the material parameters of the physics-based simulation (PBS). Built upon a differentiable cloth simulator, the optimization process is directed towards minimizing the deviation of the simulated garment shape from the target geometry. Moreover, our produced patterns meet manufacturing requirements such as left-to-right-symmetry, making them suited for reverse garment fabrication. We validate our approach on examples of different garment types, and show that our method faithfully reproduces both the draped garment shape and the sewing pattern.