PersonalTailor: Personalizing 2D Pattern Design from 3D Garment Point Clouds
This addresses the demand for personalization in garment pattern design, which is incremental as it builds on existing methods by adding personalization capabilities.
The paper tackles the problem of converting 3D garment point clouds to personalized 2D pattern designs, allowing user inputs like language or sketches, and reports that PersonalTailor excels in both personalized and standard fabrication tasks.
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.