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.

As the figure shown, SewFormer consists of three main components: (a) a visual encoder to learn sequential visual representions from the input image, (b) a two-level Transformer decoder to obtain the sewing pattern in a hierarchical manner, and (c) a stitch prediction module that recovers how different panels are stitched together to form a garment.

We showed some garment reproduction and editing results here. Each row shows an example. The first column is the input RGB image, and the second column is the sewing pattern recovered by our model. The third column is the corresponding normal map rendered based on the simulated 3D mesh and the last column shows some editing based on the recovered model.