ISP: Multi-Layered Garment Draping with Implicit Sewing Patterns

TL;DR

This paper introduces a novel parametric garment model that enables realistic, multi-layered garment draping with implicit sewing patterns, supporting rapid editing, collision detection, and layered garment reconstruction from images.

Contribution

It presents a new multi-layered garment representation combining 2D panel parameterization with implicit 3D shape modeling, overcoming limitations of previous methods.

Findings

Faster reconstruction compared to implicit surface models

Higher quality garment shape recovery

Supports layered garment editing and collision detection

Abstract

Many approaches to draping individual garments on human body models are realistic, fast, and yield outputs that are differentiable with respect to the body shape on which they are draped. However, they are either unable to handle multi-layered clothing, which is prevalent in everyday dress, or restricted to bodies in T-pose. In this paper, we introduce a parametric garment representation model that addresses these limitations. As in models used by clothing designers, each garment consists of individual 2D panels. Their 2D shape is defined by a Signed Distance Function and 3D shape by a 2D to 3D mapping. The 2D parameterization enables easy detection of potential collisions and the 3D parameterization handles complex shapes effectively. We show that this combination is faster and yields higher quality reconstructions than purely implicit surface representations, and makes the recovery of layered garments from images possible thanks to its differentiability. Furthermore, it supports rapid editing of garment shapes and texture by modifying individual 2D panels.