  Designing real and virtual garments is becoming extremely demanding with
rapidly changing fashion trends and increasing need for synthesizing realistic
dressed digital humans for various applications. This necessitates creating
simple and effective workflows to facilitate authoring sewing patterns
customized to garment and target body shapes to achieve desired looks.
Traditional workflow involves a trial-and-error procedure wherein a mannequin
is draped to judge the resultant folds and the sewing pattern iteratively
adjusted until the desired look is achieved. This requires time and experience.
Instead, we present a data-driven approach wherein the user directly indicates
desired fold patterns simply by sketching while our system estimates
corresponding garment and body shape parameters at interactive rates. The
recovered parameters can then be further edited and the updated draped garment
previewed. Technically, we achieve this via a novel shared shape space that
allows the user to seamlessly specify desired characteristics across multimodal
input {\em without} requiring to run garment simulation at design time. We
evaluate our approach qualitatively via a user study and quantitatively against
test datasets, and demonstrate how our system can generate a rich quality of
on-body garments targeted for a range of body shapes while achieving desired
fold characteristics.
