Towards Garment Sewing Pattern Reconstruction from a Single Image
This work addresses the need for automated sewing pattern recovery to enhance applications like fashion design and virtual try-on, representing an incremental advance in computer vision for garment modeling.
The paper tackles the problem of reconstructing garment sewing patterns from a single image, proposing a two-level Transformer network (Sewformer) that significantly improves prediction performance, as demonstrated through extensive experiments on a synthesized dataset of around 1M images.
Garment sewing pattern represents the intrinsic rest shape of a garment, and is the core for many applications like fashion design, virtual try-on, and digital avatars. In this work, we explore the challenging problem of recovering garment sewing patterns from daily photos for augmenting these applications. To solve the problem, we first synthesize a versatile dataset, named SewFactory, which consists of around 1M images and ground-truth sewing patterns for model training and quantitative evaluation. SewFactory covers a wide range of human poses, body shapes, and sewing patterns, and possesses realistic appearances thanks to the proposed human texture synthesis network. Then, we propose a two-level Transformer network called Sewformer, which significantly improves the sewing pattern prediction performance. Extensive experiments demonstrate that the proposed framework is effective in recovering sewing patterns and well generalizes to casually-taken human photos. Code, dataset, and pre-trained models are available at: https://sewformer.github.io.