Xiangyu Xu
Jiabin Liang
Description: SewFormer
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. SewFa
We present a new dataset, SewFactory, for sewing pattern recovery from a single image. A comprehensive comparison betwee SewFactory and other existing garment datasets can be found in the below table. Notably, SewFactory possesses high pose variability and a diverse range of garments and human textures, which effectively closes the domain gap with real-word inputs. table.GeneratedTable { width: 100%; background-color: #ffffff; border-collapse: collapse; border-top-width: 2px; border-bottom-width: 2px; borde
--> Moreover, SewFactory provides abundant ground-truth labels as shown in below, which could be potentially benefit many applications even beyond the task in this task.
Xiangyu Xu
Jiabin Liang