SPnet: Estimating Garment Sewing Patterns from a Single Image

TL;DR

This paper introduces SPnet, a method that reconstructs 3D garments from a single image by inferring sewing patterns and simulating garment deformation, enabling realistic pose adaptation.

Contribution

It proposes a novel two-stage approach that infers sewing patterns from a single image and uses physics simulation for realistic 3D garment deformation.

Findings

Accurately predicts sewing patterns from a single image.

Generates realistic 3D garments that deform naturally to new poses.

Outperforms existing methods in ablation studies.

Abstract

This paper presents a novel method for reconstructing 3D garment models from a single image of a posed user. Previous studies that have primarily focused on accurately reconstructing garment geometries to match the input garment image may often result in unnatural-looking garments when deformed for new poses. To overcome this limitation, our approach takes a different approach by inferring the fundamental shape of the garment through sewing patterns from a single image, rather than directly reconstructing 3D garments. Our method consists of two stages. Firstly, given a single image of a posed user, it predicts the garment image worn on a T-pose, representing the baseline form of the garment. Then, it estimates the sewing pattern parameters based on the T-pose garment image. By simulating the stitching and draping of the sewing pattern using physics simulation, we can generate 3D garments that can adaptively deform to arbitrary poses. The effectiveness of our method is validated through ablation studies on the major components and a comparison with other approaches.