  Garment pattern design aims to convert a 3D garment to the corresponding 2D
panels and their sewing structure. Existing methods rely either on template
fitting with heuristics and prior assumptions, or on model learning with
complicated shape parameterization. Importantly, both approaches do not allow
for personalization of the output garment, which today has increasing demands.
To fill this demand, we introduce PersonalTailor: a personalized 2D pattern
design method, where the user can input specific constraints or demands (in
language or sketch) for personal 2D panel fabrication from 3D point clouds.
PersonalTailor first learns a multi-modal panel embeddings based on
unsupervised cross-modal association and attentive fusion. It then predicts a
binary panel masks individually using a transformer encoder-decoder framework.
Extensive experiments show that our PersonalTailor excels on both personalized
and standard pattern fabrication tasks.
