  We present a novel learning method to predict the cloth deformation for
skeleton-based characters with a two-stream network. The characters processed
in our approach are not limited to humans, and can be other skeletal-based
representations of non-human targets such as fish or pets. We use a novel
network architecture which consists of skeleton-based and mesh-based residual
networks to learn the coarse and wrinkle features as the overall residual from
the template cloth mesh. Our network is used to predict the deformation for
loose or tight-fitting clothing or dresses. We ensure that the memory footprint
of our network is low, and thereby result in reduced storage and computational
requirements. In practice, our prediction for a single cloth mesh for the
skeleton-based character takes about 7 milliseconds on an NVIDIA GeForce RTX
3090 GPU. Compared with prior methods, our network can generate fine
deformation results with details and wrinkles.
