Spatially Adaptive Cloth Regression with Implicit Neural Representations
Lei Shu Vinicius Azevedo Barbara Solenthaler Markus Gross
The accurate representation of fine-detailed cloth wrinkles poses significant
challenges in computer graphics. The inherently non-uniform structure of cloth
wrinkles mandates the employment of intricate discretization strategies, which
are frequently characterized by high computational demands and complex
methodologies. Addressing this, the research introduced in this paper
elucidates a novel anisotropic cloth regression technique that capitalizes on
the potential of implicit neural representations of surfaces. Our first core
contribution is an innovative mesh-free sampling approach, crafted to reduce
the reliance on traditional mesh structures, thereby offering greater
flexibility and accuracy in capturing fine cloth details. Our second
contribution is a novel adversarial training scheme, which is designed
meticulously to strike a harmonious balance between the sampling and simulation
objectives. The adversarial approach ensures that the wrinkles are represented
with high fidelity, while also maintaining computational efficiency. Our
results showcase through various cloth-object interaction scenarios that our
method, given the same memory constraints, consistently surpasses traditional
discrete representations, particularly when modelling highly-detailed localized
wrinkles.