  The capability to generate simulation-ready garment models from 3D shapes of
clothed humans will significantly enhance the interpretability of captured
geometry of real garments, as well as their faithful reproduction in the
virtual world. This will have notable impact on fields like shape capture in
social VR, and virtual try-on in the fashion industry. To align with the
garment modeling process standardized by the fashion industry as well as cloth
simulation softwares, it is required to recover 2D patterns. This involves an
inverse garment design problem, which is the focus of our work here: Starting
with an arbitrary target garment geometry, our system estimates an animatable
garment model by automatically adjusting its corresponding 2D template pattern,
along with the material parameters of the physics-based simulation (PBS). Built
upon a differentiable cloth simulator, the optimization process is directed
towards minimizing the deviation of the simulated garment shape from the target
geometry. Moreover, our produced patterns meet manufacturing requirements such
as left-to-right-symmetry, making them suited for reverse garment fabrication.
We validate our approach on examples of different garment types, and show that
our method faithfully reproduces both the draped garment shape and the sewing
pattern.
