  Clothing plays a vital role in real life and hence, is also important for
virtual realities and virtual applications, such as online retail, virtual
try-on, and real-time digital avatar interactions. However, choosing the
correct parameters to generate realistic clothing requires expert knowledge and
is often an arduous manual process. To alleviate this issue, we develop a
pipeline for automatically determining the static material parameters required
to simulate clothing of a particular material based on easily captured
real-world fabrics. We use differentiable simulation to find an optimal set of
parameters that minimizes the difference between simulated cloth and deformed
target cloth. Our novel well-suited loss function is optimized through
non-linear least squares. We designed our objective function to capture
material-specific behavior, resulting in similar values for different wrinkle
configurations of the same material. While existing methods carefully design
experiments to isolate stretch parameters from bending modes, we embrace that
stretching fabrics causes wrinkling. We estimate bending first, given that
membrane stiffness has little effect on bending. Furthermore, our pipeline
decouples the capture method from the optimization by registering a template
mesh to the scanned data. These choices simplify the capture system and allow
for wrinkles in scanned fabrics. We use a differentiable extended
position-based dynamics (XPBD) cloth simulator, which is capable of real-time
simulation. We demonstrate our method on captured data of three different
real-world fabrics and on three digital fabrics produced by a third-party
simulator.
