  Delicate cloth simulations have long been desired in computer graphics.
Various methods were proposed to improve engaged force interactions, collision
handling, and numerical integrations. Deep learning has the potential to
achieve fast and real-time simulation, but common neural network structures
often demand many parameters to capture cloth dynamics. This paper proposes a
physics-embedded learning framework that directly encodes physical features of
cloth simulation. The convolutional neural network is used to represent spatial
correlations of the mass-spring system, after which three branches are designed
to learn linear, nonlinear, and time derivate features of cloth physics. The
framework can also integrate with other external forces and collision handling
through either traditional simulators or sub neural networks. The model is
tested across different cloth animation cases, without training with new data.
Agreement with baselines and predictive realism successfully validate its
generalization ability. Inference efficiency of the proposed model also defeats
traditional physics simulation. This framework is also designed to easily
integrate with other visual refinement techniques like wrinkle carving, which
leaves significant chances to incorporate prevailing macing learning techniques
in 3D cloth amination.
