SPnet: Estimating Garment Sewing Patterns from a Single Image
Seungchan Lim Sumin Kim Sung-Hee Lee
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.