CTSN: Predicting Cloth Deformation for Skeleton-based Characters with a Two-stream Skinning Network
This work addresses the need for efficient and detailed cloth simulation in animation and gaming, offering a domain-specific solution with incremental improvements over prior methods.
The paper tackles the problem of predicting cloth deformation for skeleton-based characters, including non-human ones, using a two-stream network that learns coarse and wrinkle features from a template mesh, achieving a prediction time of about 7 milliseconds per mesh on an NVIDIA GeForce RTX 3090 GPU.
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.