Self-supervised Garment Dynamics with Persistent Wrinkles

Self-supervised neural garment simulation has become pop-ular due to its computational efficiency, good visual realism, and noreliance on training data. However, existing methods greatly simplifythe mechanical properties of fabrics, ignoring persistent wrinkles causedby plasticity. Although this simplification allows for modeling of purelyelastic material and simple training via energy minimization, the lackof believable wrinkles adversely affects the visual realism. Therefore, weintroduce the first self-supervised neural garment simulator that explic-itly models persistent wrinkles. This is accomplished through a novelphysics-inspired loss function, which turns learning into a moving energyminimization problem to mimic plasticity. However, this requires learn-ing to use a changing loss function, which causes difficulties in trainingbecause the loss function changes during optimization. To this end, wepropose a new physics-inspired curriculum learning scheme where thetarget material for learning gradually changes from pure elasticity toelasto-plasticity, allowing the loss function and the learnable parametersto jointly converge. Through a comprehensive evaluation, we show thatfor the first time, self-supervised learning models can generate naturalpersistent wrinkles, outperforming existing methods on a variety of gar-ments, body shapes, and body motions, according to a range of metrics.Our code is publicly available at https://github.com/realcrane/EPNet